Statistics

Cao Pinna, L., Miller, C., Scott, M. (2024) Latent Dirichlet Allocation and Hidden Markov Models to Identify Public Perception of Sustainability in Social Media Data.

Mair, C., Macaulay, C., Olowe, E., Beekman, E., Brown, K. (2024) Co-creating feedback dialogue tools through course evaluations. Journal of Educational Innovation, Partnership and Change, 10,

Abroshan, M., Elliott, A., Khalili, M. M. (2024) Imposing Fairness Constraints in Synthetic Data Generation.

Cobbold, C., Dawes, A., Schwartz, E. J., Tyson, R. C. (2024) Exploring interdisciplinary appointments: a focused perspective on mathematical biology. Notices of the American Mathematical Society,

Beekman, E., Brown, K., Olowe, E., Callum, M., Mair, C. (2024) How students and staff perceive course evaluations and engage with class reps.

Wilkie, C., Ray, S., Miller, C., Scott, M. (2024) Investigating River Water Quality Using Network Distance Based Spatiotemporal Flexible Regression.

Li, R., Mair, C. (2024) What Would it Take to Decolonise STEM?

Otto, P. (2024) A multivariate spatial and spatiotemporal ARCH model. Spatial Statistics, 60, (doi: 10.1016/j.spasta.2024.100823)

Li, X., Li, Z., Xie, J., Yang, X., Xue, J.-H., Ma, Z. (2024) Self-reconstruction network for fine-grained few-shot classification. Pattern Recognition, (doi: 10.1016/j.patcog.2024.110485)

Muegge, R., Jack, E., Dean, N., Lee, D. (2024) COVID-19 vaccine fatigue in Scotland: how do the trends in attrition rates for the second and third doses differ by age, sex, and council area? Journal of the Royal Statistical Society: Series A (Statistics in Society),

Mohammadi, R., Taleai, M., Otto, P., Sester, M. (2024) Analyzing urban crash incidents: an advanced endogenous approach using spatiotemporal weights matrix. Transactions in GIS, 28, pp. 368-410. (doi: 10.1111/tgis.13138)

Yang, Y., Husmeier, D., Gao, H., Berry, C., Carrick, D., Radjenovic, A. (2024) Automatic detection of myocardial ischaemia using generalisable spatio-temporal hierarchical Bayesian modelling of DCE-MRI. Computerized Medical Imaging and Graphics, 113, (doi: 10.1016/j.compmedimag.2024.102333)

Chadwick, F. J., Haydon, D. T., Husmeier, D., Ovaskainen, O., Matthiopoulos, J. (2024) LIES of omission: complex observation processes in ecology. Trends in Ecology and Evolution, 39, pp. 368-380. (doi: 10.1016/j.tree.2023.10.009)

Di Campli San Vito, P., Yang, X., Ross, J., Shakeri, G., Brewster, S., Venkatesh, S., Street, A., Fachner, J., Fernie, P., Muller-Rodriguez, L., Hung Hsu, M., Odell-Miller, H., Shaji, H., Itaborai, P., Farina, N., Banerjee, S., Kirke, A., Miranda, E. (2024) RadioMe: Adaptive Radio with Music Intervention and Reminder System for People with Dementia in Their Own Home. (doi: 10.1145/3652920.3653055)

Cao Pinna, L., Gallien, L., Pollock, L. J., Axmanová, I., Chytrý, M., Malavasi, M., Acosta, A. T. R., Campos, J. A., Carboni, M. (2024) Plant invasion in Mediterranean Europe: current hotspots and future scenarios. Ecography, (doi: 10.1111/ecog.07085)

de Vilmarest, J., Browell, J., Fasiolo, M., Goude, Y., Wintenberger, O. (2024) Adaptive probabilistic forecasting of electricity (net-)load. IEEE Transactions on Power Systems, 39, pp. 4154-4163. (doi: 10.1109/TPWRS.2023.3310280)

Lee, D. (2024) Computationally efficient localised spatial smoothing of disease rates using anisotropic basis functions and penalised regression fitting. Spatial Statistics, 59, (doi: 10.1016/j.spasta.2023.100796)

Yang, X., Guo, Y., Dong, M., Xue, J.-H. (2024) Towards certified robustness of distance metric learning. IEEE Transactions on Neural Networks and Learning Systems, 35, pp. 3834-3844. (doi: 10.1109/TNNLS.2022.3199902)

Jack, E., Alexander, C., Jones, E. M. (2024) Levelling Up Learning: Exploring the Impact of Gamification in Flipped Classrooms. arXiv, (doi: 10.48550/arXiv.2402.18313)

Dunbar, E., Scott, E. M., Tripney, B. G. (2024) Carbon isotope changes through the recent past: f14c and δ13c values in single barley grain from 1852 to 2020. Radiocarbon, (doi: 10.1017/rdc.2024.8)

McNealis, V., Moodie, E. E.M., Dean, N. (2024) Revisiting the effects of maternal education on adolescents' academic performance: Doubly robust estimation in a network-based observational study. Journal of the Royal Statistical Society: Series C (Applied Statistics), (doi: 10.1093/jrsssc/qlae008)

Otto, P., Fusta Moro, A., Rodeschini, J., Shaboviq, Q., Ignaccolo, R., Golini, N., Cameletti, M., Maranzano, P., Finazzi, F., Fassò, A. (2024) Spatiotemporal modelling of PM2.5 concentrations in Lombardy (Italy): a comparative study. Environmental and Ecological Statistics, (doi: 10.1007/s10651-023-00589-0)

Bonner, S. J., Zhang, W., Mu, J. (2024) On the identifiability of the trinomial model for mark‐recapture‐recovery studies. Environmetrics, 35, (doi: 10.1002/env.2827)

Rocha, A. S., de Cássia Ribeiro-Silva, R., Silva, J. F.M., Pinto, E. J., Silva, N. J., Paixao, E. S., Fiaccone, R. L., Kac, G., Rodrigues, L. C., Anderson, C., Barreto, M. L. (2024) Postnatal growth in small vulnerable newborns: a longitudinal study of 2 million Brazilians using routine register-based linked data. American Journal of Clinical Nutrition, 119, pp. 444-455. (doi: 10.1016/j.ajcnut.2023.12.009)

Malinovskaya, A., Mozharovskyi, P., Otto, P. (2024) Statistical process monitoring of artificial neural networks. Technometrics, 66, pp. 104-117. (doi: 10.1080/00401706.2023.2239886)

Mattera, R., Otto, P. (2024) Network log-ARCH models for forecasting stock market volatility. International Journal of Forecasting, (doi: 10.1016/j.ijforecast.2024.01.002)

Ouyang, R., Elliott, A., Limnios, S., Cucuringu, M., Reinert, G. (2024) L2G2G: A scalable local-to-global network embedding with graph autoencoders. Springer

Fülle, M. J., Otto, P. (2024) Spatial GARCH models for unknown spatial locations – an application to financial stock returns. Spatial Economic Analysis, 19, pp. 92-105. (doi: 10.1080/17421772.2023.2237067)

Lilly, J. M. et al. (2024) Migration patterns and navigation cues of Atlantic salmon post‐smolts migrating from 12 rivers through the coastal zones around the Irish Sea. Journal of Fish Biology, 104, pp. 265-283. (doi: 10.1111/jfb.15591)

Vermeire, T. G., Hoet, P., Ion, R.-M., Krätke, R., Proykova, A., Scott, M., de Jong, W. H., SCHEER, (2024) Opinion of the Scientific Committee on health, environmental and emerging risks on the safety of titanium dioxide in toys. Regulatory Toxicology and Pharmacology, 146, (doi: 10.1016/j.yrtph.2023.105527)

Shreves, K. V., Saraiva, M., Ruba, T., Miller, C., Scott, E. M., McLaggan, D., van West, P. (2024) Specific phylotypes of Saprolegnia parasitica associated with Atlantic salmon freshwater aquaculture. Journal of Fungi, 10, (doi: 10.3390/jof10010057)

Mair, C., Fu, J. (2024) Learning Development in Statistics Education.

Müller, M., Vlaar, T., Rolnick, D., Hein, M. (2024) Normalization Layers Are All That Sharpness-Aware Minimization Needs.

Donaldson, D. L., Browell, J., Gilbert, C. (2023) Predicting the magnitude and timing of peak electricity demand: A competition case study. IET Smart Grid, (doi: 10.1049/stg2.12152)

Zhou, J., Husmeier, D., Gao, H., Yin, C., Qiu, C., Jing, X., Qi, Y., Liu, W. (2023) Bayesian inversion of frequency-domain airborne EM data with spatial correlation prior information. IEEE Transactions on Geoscience and Remote Sensing, 62, (doi: 10.1109/TGRS.2023.3344946)

Dalton, D., Husmeier, D., Gao, H. (2023) Physics-informed graph neural network emulation of soft-tissue mechanics computer methods in applied mechanics and engineering. Computer Methods in Applied Mechanics and Engineering, 417, (doi: 10.1016/j.cma.2023.116351)

Law, S., Hasegawa, R., Paige, B., Russell, C., Elliott, A. (2023) Explaining holistic image regressors and classifiers in urban analytics with plausible counterfactuals. International Journal of Geographical Information Science, 37, pp. 2575-2596. (doi: 10.1080/13658816.2023.2214592)

Bowman, A. W., Christensen, J. R., Dagneau, C., Kavousanaki, D., Millar, K., Moore, J. (2023) Human hair from the wreck of HMS Erebus of the Franklin Expedition, 1845: elemental chemistry revealed by double-ablation LA-ICP-MS. Journal of Archaeological Science: Reports, 52, (doi: 10.1016/j.jasrep.2023.104270)

Lee, D. (2023) Identifying boundaries in spatially continuous risk surfaces from spatially aggregated disease count data. Annals of Applied Statistics, 17, pp. 3153-3172. (doi: 10.1214/23-AOAS1755)

Di Napoli, M., Tanyas, H., Castro-Camilo, D., Calcaterra, D., Cevasco, A., Di Martire, D., Pepe, G., Brandolini, P., Lombardo, L. (2023) On the estimation of landslide intensity, hazard and density via data-driven models. Natural Hazards, 119, pp. 1513-1530. (doi: 10.1007/s11069-023-06153-0)

Hjelmskog, A., Toney, J., Scott, M., Crawford, J., Hasan, C., Winterbottom, J., Meier, P. (2023) Thriving Glasgow Portrait: A shared vision for a healthy, equitable and sustainable future. (doi: 10.36399/gla.pubs.309671)

Foster, H. M.E., Gill, J. M.R., Mair, F. S., Celis-Morales, C. A., Jani, B. D., Nicholl, B. I., Lee, D., O'Donnell, C. A. (2023) Social connection and mortality in UK Biobank: a prospective cohort analysis. BMC Medicine, 21, (doi: 10.1186/s12916-023-03055-7)

Otto, P., Doğan, O., Taşpınar, S. (2023) A dynamic spatiotemporal stochastic volatility model with an application to environmental risks. Econometrics and Statistics, (doi: 10.1016/j.ecosta.2023.11.002)

Gioia, V., Fasiolo, M., Browell, J., Bellio, R. (2023) Additive covariance matrix models: modelling regional electricity net-demand in Great Britain. arXiv, (doi: 10.48550/ARXIV.2211.07451)

Lee, D., Walton, H., Evangelopoulos, D., Katsouyanni, K., Gowers, A. M., Shaddick, G., Mitsakou, C. (2023) Health impact assessment for air pollution in the presence of regional variation in effect sizes: the implications of using different meta-analytic approaches. Environmental Pollution, 336, (doi: 10.1016/j.envpol.2023.122465)

Van Mechelen, I., Boulesteix, A.‐L., Dangl, R., Dean, N., Hennig, C., Leisch, F., Steinley, D., Warrens, M. J. (2023) A white paper on good research practices in benchmarking: the case of cluster analysis. WIREs Data Mining and Knowledge Discovery, 13, (doi: 10.1002/widm.1511)

Gemmell, A. J., Brown, C. M., Ray, S., Small, A. (2023) Quantitative uptake in 99mTc-EDDA/HYNIC-TOC somatostatin receptor imaging – the effect of long-acting release somatostatin analogue therapy. Nuclear Medicine Communications, 44, pp. 944-952. (doi: 10.1097/MNM.0000000000001746)

Mair, C., Brown, K., Beekman, E., Olowe, E., Macaulay, C. (2023) Nurturing learning development through student feedback. Journal of Learning Development in Higher Education, 29, (doi: 10.47408/jldhe.vi29.1116)

Cheng, F., Yang, X. (2023) Self-Supervised Cross-Encoder for Diagnosis of Alzheimer's Disease.

Otto, P., Doğan, O., Taşpınar, S. (2023) Dynamic spatiotemporal ARCH models. Spatial Economic Analysis, (doi: 10.1080/17421772.2023.2254817)

Zhang, W., Ray, S. (2023) Deep Probability Contour Framework for Tumour Segmentation and Dose Painting in PET Images. (doi: 10.1007/978-3-031-43901-8_51)

Carturan, B. S., Siewe, N., Cobbold, C. A., Tyson, R. C. (2023) Bumble bee pollination and the wildflower/crop trade-off: When do wildflower enhancements improve crop yield? Ecological Modelling, 484, (doi: 10.1016/j.ecolmodel.2023.110447)

Cantoni, D., Wilkie, C., Bentley, E. M., Mayora-Neto, M., Wright, E., Scott, S., Ray, S., Castillo-Olivares, J., Heeney, J. L., Mattiuzzo, G., Temperton, N. J. (2023) Correlation between pseudotyped virus and authentic virus neutralisation assays, a systematic review and meta-analysis of the literature. Frontiers in Immunology, 14, (doi: 10.3389/fimmu.2023.1184362)

Zhang, W., Ray, S. (2023) From coarse to fine: a deep 3D probability volume contour framework for tumor segmentation and dose painting in PET images. Frontiers In Radiology, 3, (doi: 10.3389/fradi.2023.1225215)

Sekhar Sen, I., Nizam, S., Ansari, A., Bowes, M., Choudhary, B., Glendell, M., Ray, S., Scott, M., Miller, C., Wilkie, C., Sinha, R. (2023) Geochemical evolution of dissolved trace elements in space and time in the Ramganga River, India. Environmental Monitoring and Assessment, 195, (doi: 10.1007/s10661-023-11665-0)

Mair, C. (2023) Student and staff perceptions of student evaluations.

Scott, E. M., Naysmith, P., Dunbar, E. (2023) Preliminary results from the Glasgow International Radiocarbon Intercomparison (GIRI) Radiocarbon, (doi: 10.1017/RDC.2023.64)

Di Campli San Vito, P., Shakeri, G., Ross, J., Yang, X., Brewster, S. (2023) Development of a Real-Time Stress Detection System for Older Adults with Heart Rate Data. (doi: 10.1145/3594806.3594817)

Ge, Y., Husmeier, D., Lazarus, A., Rabbani, A., Gao, H. (2023) Bayesian inference of cardiac models emulated with a time series Gaussian process. International Aset Inc.

Pascall, D. J. et al. (2023) Directions of change in intrinsic case severity across successive SARS-CoV-2 variant waves have been inconsistent. Journal of Infection, 87, pp. 128-135. (doi: 10.1016/j.jinf.2023.05.019)

Bass, A. M., Coleman, M., Waldron, S., Scott, M. (2023) Dissolved organic carbon export in a small, disturbed peat catchment: insights from long-term, high-resolution, sensor-based monitoring. Limnology and Oceanography, 68, pp. 1750-1761. (doi: 10.1002/lno.12382)

McCartney, G., Hoggett, R., Walsh, D., Lee, D. (2023) How important is it to avoid indices of deprivation that include health variables in analyses of health inequalities? Public Health, 221, pp. 175-180. (doi: 10.1016/j.puhe.2023.06.028)

Bronk Ramsey, C. et al. (2023) Development of the IntCal database. Radiocarbon, (doi: 10.1017/RDC.2023.53)

Colombo, P., Miller, C., O'Donnell, R., Yang, X. (2023) A Multifidelity Framework for Wind Speed Data.

Radvanyi, P., Miller, C., Alexander, C., Low, M., Jones, W. R., Rock, L. (2023) Computationally Efficient Ranking of Groundwater Monitoring Locations.

Olowe, E., Macaulay, C., Brown, K., Beekman, E., Mair, C. (2023) Developing Feedback Literacy Minus the Assessment.

Tripney, B.G., Dunbar, E., Scott, E.M., Naysmith, P. (2023) Routine quality assurance in the SUERC Radiocarbon Laboratory. Radiocarbon, (doi: 10.1017/RDC.2023.45)

McBride, R., Wandy, J., Weidt, S., Rogers, S., Davies, V., Daly, R., Bryson, K. (2023) TopNEXt: automatic DDA exclusion framework for multi-sample mass spectrometry experiments. Bioinformatics, 39, (doi: 10.1093/bioinformatics/btad406)

Di Campli San Vito, P., Yang, X., Brewster, S., Street, A., Fachner, J., Fernie, P., Muller-Rodriguez, L., Hung Hsu, M., Odell-Miller, H., Shaji, H., Itaborai, P., Evison, B., Farina, N., Banerjee, S., Kirke, A., Miranda, E. (2023) RadioMe: Adaptive Radio to Support People with Mild Dementia in Their Own Home. (doi: 10.3233/FAIA230114)

Li, L., Gupta, M., Macaulay, V., Mukhopadhyay, I. (2023) Bayesian GWAS with Evolutionary Monte Carlo.

Li, X., Yang, X., Ma, Z., Xue, J.-H. (2023) Deep metric learning for few-shot image classification: a review of recent developments. Pattern Recognition, 138, (doi: 10.1016/j.patcog.2023.109381)

Rabbani, A., Gao, H., Lazarus, A., Dalton, D., Ge, Y., Mangion, K., Berry, C., Husmeier, D. (2023) Image-based estimation of the left ventricular cavity volume using deep learning and Gaussian process with cardio-mechanical applications. Computerized Medical Imaging and Graphics, 106, (doi: 10.1016/j.compmedimag.2023.102203)

Gilbert, C., Browell, J., Stephen, B. (2023) Probabilistic load forecasting for the low voltage network: forecast fusion and daily peaks. Sustainable Energy, Grids and Networks, 34, (doi: 10.1016/j.segan.2023.100998)

Paradinas, I., Illian, J., Smout, S. (2023) Understanding spatial effects in species distribution models. PLoS ONE, 18, (doi: 10.1371/journal.pone.0285463)

Ho, A. et al. (2023) Adeno-associated virus 2 infection in children with non-A-E hepatitis. Nature, 617, pp. 555-563. (doi: 10.1038/s41586-023-05948-2)

Mair, C., Brown, K., Beekman, E., Olowe, E., Macaulay, C. (2023) Course Evaluations as a Feedback Dialogue Tool.

Glendell, M., Sinha, R., Choudhary, B., Singh, M., Ray, S. (2023) Probabilistic Modelling of Water Quality in the Ramganga River, India, Informed by Sparce Observational Data. (doi: 10.5194/egusphere-egu23-7990)

Neethipathi, D. K., Beniwal, A., Ganguly, P., Bass, A., Scott, M., Dahiya, R. (2023) Electrochemical Detection of Fe2+ ions in Water using 2-Dimensional g-C3N4 modified Glassy Carbon Electrode-based Sensor. (doi: 10.1109/APSCON56343.2023.10101086)

Neethipathi, D. K., Beniwal, A., Bass, A., Scott, M., Dahiya, R. (2023) MoS2 modified screen printed carbon electrode based flexible electrochemical sensor for detection of copper ions in water. IEEE Sensors Journal, 23, pp. 8146-8153. (doi: 10.1109/JSEN.2023.3257188)

Pascall, D. J. et al. (2023) The SARS-CoV-2 Alpha variant was associated with increased clinical severity of COVID-19 in Scotland: a genomics-based retrospective cohort analysis. PLoS ONE, 18, (doi: 10.1371/journal.pone.0284187)

Otto, P., Steinert, R. (2023) Estimation of the spatial weighting matrix for spatiotemporal data under the presence of structural breaks. Journal of Computational and Graphical Statistics, 32, pp. 696-711. (doi: 10.1080/10618600.2022.2107530)

Villejo, S. J., Illian, J. B., Swallow, B. (2023) Data fusion in a two-stage spatio-temporal model using the INLA-SPDE approach. Spatial Statistics, 54, (doi: 10.1016/j.spasta.2023.100744)

McCartney, G., Hoggett, R., Walsh, D., Lee, D. (2023) How well do area-based deprivation indices identify income- and employment-deprived individuals across Great Britain today? Public Health, 217, pp. 22-25. (doi: 10.1016/j.puhe.2023.01.020)

Paun, I., Husmeier, D., Torney, C. J. (2023) Stochastic variational inference for scalable non-stationary Gaussian process regression. Statistics and Computing, 33, (doi: 10.1007/s11222-023-10210-w)

Mair, C. (2023) Developing Graduate Attributes by Engaging Students with SoTL.

Novellino, A., Ciurean, R., Bryce, E., Castro-Camilo, D., Lombardo, L. (2023) Mitigating Landslides Impact in Scotland - MLIS. Summary Report.

Nguyen, H. D., Gupta, M. (2023) Finite sample inference for empirical Bayesian methods. Scandinavian Journal of Statistics, (doi: 10.1111/sjos.12643)

Fassò, A., Rodeschini, J., Fusta Moro, A., Shaboviq, Q., Maranzano, P., Cameletti, M., Finazzi, F., Golini, N., Ignaccolo, R., Otto, P. (2023) Agrimonia: a dataset on livestock, meteorology and air quality in the Lombardy region, Italy. Scientific Data, 10, (doi: 10.1038/s41597-023-02034-0)

Wandy, J., Mcbride, R., Rogers, S., Terzis, N., Weidt, S., van der Hooft, J. J.J., Bryson, K., Daly, R., Davies, V. (2023) Simulated-to-real benchmarking of acquisition methods in untargeted metabolomics. Frontiers in Molecular Biosciences, 10, (doi: 10.3389/fmolb.2023.1130781)

Harvey, W. T., Davies, V., Daniels, R. S., Whittaker, L., Gregory, V., Hay, A. J., Husmeier, D., McCauley, J. W., Reeve, R. (2023) A Bayesian approach to incorporate structural data into the mapping of genotype to antigenic phenotype of influenza A(H3N2) viruses. PLoS Computational Biology, 19, (doi: 10.1371/journal.pcbi.1010885)

Scott, E. M. (2023) Framing data science, analytics and statistics around the digital earth concept. Environmetrics, 34, (doi: 10.1002/env.2732)

McEwan, M. P., Jack, E., Alexander, C., Bock, M. (2023) From statistics anxiety to SoTL: how a scholarly enquiry led to professional growth. Open Scholarship of Teaching and Learning, 2, pp. 1-15. (doi: 10.56230/osotl.55)

Hu, M., Stephen, B., Browell, J., Haben, S., Wallom, D. C. H. (2023) Impacts of building load dispersion level on its load forecasting accuracy: Data or algorithms? Importance of reliability and interpretability in machine learning. Energy and Buildings, 285, (doi: 10.1016/j.enbuild.2023.112896)

Tragakis, A., Kaul, C., Murray-Smith, R., Husmeier, D. (2023) The Fully Convolutional Transformer for Medical Image Segmentation. (doi: 10.1109/WACV56688.2023.00365)

Harke, F., Otto, P. (2023) Solar self-sufficient households as a driving factor for sustainability transformation. Sustainability, 15, (doi: 10.3390/su15032734)

Chen, Y., Shi, Y., Dong, M., Yang, X., Li, D., Wang, Y., Dick, R., Lv, Q., Zhao, Y., Yang, F., Gu, N., Shang, L. (2023) Over-parameterized Model Optimization with Polyak-Łojasiewicz Condition.

Jones, K.A., Paterson, C.A., Ray, S., Motherwell, D.W., Hamilton, D.J., Small, A.D., Martin, W., Goodfield, N.E.R. (2023) Beta-blockers and mechanical dyssynchrony in heart failure assessed by radionuclide ventriculography. Journal of Nuclear Cardiology, 30, pp. 193-200. (doi: 10.1007/s12350-022-03142-x)

Fang, Y., Niu, M., Cheung, P., Lin, L. (2023) Extrinsic Bayesian optimization on manifolds. Algorithms, 16, (doi: 10.3390/a16020117)

Muegge, R., Dean, N., Jack, E., Lee, D. (2023) National lockdowns in England: the same restrictions for all, but do the impacts on COVID-19 mortality risks vary geographically? Spatial and Spatio-Temporal Epidemiology, 44, (doi: 10.1016/j.sste.2022.100559)

Otto, P., Otto, P. (2023) What’s in a name? Significance, 20, pp. 34-37. (doi: 10.1093/jrssig/qmad010)

Dangerfield, C. E. et al. (2023) Getting the most out of maths: How to coordinate mathematical modelling research to support a pandemic, lessons learnt from three initiatives that were part of the COVID-19 response in the UK. Journal of Theoretical Biology, 557, (doi: 10.1016/j.jtbi.2022.111332)

Zhang, W., Chipperfield, J. D., Illian, J. B., Dupont, P., Milleret, C., de Valpine, P., Bischof, R. (2023) A flexible and efficient Bayesian implementation of point process models for spatial capture-recapture data. Ecology, 104, (doi: 10.1002/ecy.3887)

Laxton, M. R., Rodríguez de Rivera, Ó., Soriano‐Redondo, A., Illian, J. B. (2023) Balancing structural complexity with ecological insight in spatio‐temporal species distribution models. Methods in Ecology and Evolution, 14, pp. 162-172. (doi: 10.1111/2041-210x.13957)

Andrade, R., Cobbold, C. A. (2023) Heterogeneity in behaviour and movement can influence the stability of predator–prey periodic travelling waves. Bulletin of Mathematical Biology, 85, (doi: 10.1007/s11538-022-01101-8)

Gaskell, J., Campioni, N., Morales, J. M., Husmeier, D., Torney, C. J. (2023) Inferring the interaction rules of complex systems with graph neural networks and approximate Bayesian computation. Journal of the Royal Society: Interface, 20, (doi: 10.1098/rsif.2022.0676)

Stoner, O., Economou, T., Torres, R., Ashton, I., Brown, A. R. (2023) Quantifying spatio-temporal risk of harmful algal blooms and their impacts on bivalve shellfish mariculture using a data-driven modelling approach. Harmful Algae, 121, (doi: 10.1016/j.hal.2022.102363)

Borowska, A., King, R. (2023) Semi-complete data augmentation for efficient state space model fitting. Journal of Computational and Graphical Statistics, 32, pp. 19-35. (doi: 10.1080/10618600.2022.2077350)

Ryan, W., Husmeier, D., Rolinski, O. J., Vyshemirsky, V. (2023) Bayesian Model Selection and Emulation for Protein Fluorescence. (doi: 10.11159/icsta23.153)

Stoner, O., Halliday, A., Economou, T. (2023) Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method. Biometrics, (doi: 10.1111/biom.13810)

Niu, M., Dai, Z., Cheung, P., Wang, Y. (2023) Intrinsic Gaussian process on unknown manifolds with probabilistic metrics. Journal of Machine Learning Research, 24, pp. 1-42.

Jack, E., Mair, C. (2023) Investigating Readiness for Remote Learning in Three Cohorts of Students. (doi: 10.52041/iase.icots11.T14C2)

Jack, E., Alexander, C., McArthur, D., Mair, C. (2023) Reflections on designing and delivering an online distance learning programme in the mathematical sciences. MSOR Connections, 21, pp. 25-33. (doi: 10.21100/msor.v21i2.1397)

Shi, Y., Chen, Y., Dong, M., Yang, X., Li, D., Wang, Y., Dick, R. P., Lv, Q., Zhao, Y., Yang, F., Lu, T., Gu, N., Shang, L. (2023) Train Faster, Perform Better: Modular Adaptive Training in Over-Parameterized Models.

Scott, M. (2023) Uncertainty and error in radiocarbon dating. Elsevier

Song, Q., Peng, Z., Ji, L., Yang, X., Li, X. (2022) Dual Prototypical Network for Robust Few-shot Image Classification. (doi: 10.23919/APSIPAASC55919.2022.9979898)

Li, Z., Wang, L., Ding, S., Yang, X., Li, X. (2022) Few-Shot Classification With Feature Reconstruction Bias. (doi: 10.23919/APSIPAASC55919.2022.9980086)

Limnios, S., Elliott, A., Cucuringu, M., Reinert, G. D. (2022) Random Walk based Conditional Generative Model for Temporal Networks with Attributes.

Houssiau, F., Jordon, J., Cohen, S., Elliott, A., Geddes, J., Mole, C., Rangel-Smith, C., Szpruch, L. (2022) TAPAS: a Toolbox for Adversarial Privacy Auditing of Synthetic Data.

Robinson, J. I., Md Yusof, M. Y., Davies, V., Wild, D., Morgan, M., Taylor, J. C., El-Sherbiny, Y., Morris, D. L., Liu, L., Rawstron, A. C., Buch, M. H., Plant, D., Cordell, H. J., Isaacs, J. D., Bruce, I. N., Emery, P., Barton, A., Vyse, T. J., Barrett, J. H., Vital, E. M., Morgan, A. W. (2022) Comprehensive genetic and functional analyses of Fc gamma receptors influence on response to rituximab therapy for autoimmunity. EBioMedicine, 86, (doi: 10.1016/j.ebiom.2022.104343)

Clarkson, J., Cucuringu, M., Elliott, A., Reinert, G. (2022) DAMNETS: A Deep Autoregressive Model for Generating Markovian Network Time Series.

Paun, I., Husmeier, D., Hopcraft, J. G. C., Masolele, M. M., Torney, C. J. (2022) Inferring spatially varying animal movement characteristics using a hierarchical continuous-time velocity model. Ecology Letters, 25, pp. 2726-2738. (doi: 10.1111/ele.14117)

Zhang, W., Bonner, S. J., McCrea, R. (2022) Latent multinomial models for extended batch-mark data. Biometrics, (doi: 10.1111/biom.13789)

Sullivan, M. K., Carrero, J.-J., Jani, B. D., Anderson, C., McConnachie, A., Hanlon, P., Nitsch, D., McAllister, D. A., Mair, F. S., Mark, P. B., Gasparini, A. (2022) The presence and impact of multimorbidity clusters on adverse outcomes across the spectrum of kidney function. BMC Medicine, 20, (doi: 10.1186/s12916-022-02628-2)

Yang, Y., Gao, H., Berry, C., Carrick, D., Radjenovic, A., Husmeier, D. (2022) Classification of myocardial blood flow based on dynamic contrast-enhanced magnetic resonance imaging using hierarchical Bayesian models. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71, pp. 1085-1115. (doi: 10.1111/rssc.12568)

Dalton, D., Gao, H., Husmeier, D. (2022) Emulation of cardiac mechanics using Graph Neural Networks. Computer Methods in Applied Mechanics and Engineering, 401, (doi: 10.1016/j.cma.2022.115645)

Harris, C., Brummitt, N., Cobbold, C. A., Reeve, R. (2022) Strong phylogenetic signals in global plant bioclimatic envelopes. Global Ecology and Biogeography, 31, pp. 2191-2203. (doi: 10.1111/geb.13564)

Aldossari, S., Husmeier, D., Matthiopoulos, J. (2022) Transferable species distribution modelling: comparative performance of Generalised Functional Response models. Ecological Informatics, 71, (doi: 10.1016/j.ecoinf.2022.101803)

Ray, S., Banerjee, A., Swift, A., Fanstone, J. W., Mamalakis, M., Vorselaars, B., Wilkie, C., Cole, J., Mackenzie, L. S., Weeks, S. (2022) A robust COVID-19 mortality prediction calculator based on Lymphocyte count, Urea, C-Reactive Protein, Age and Sex (LUCAS) with chest x-rays. Scientific Reports, 12, (doi: 10.1038/s41598-022-21803-2)

Lavender, E., Aleynik, D., Dodd, J., Illian, J., James, M., Smout, S., Thorburn, J. (2022) Benthic animal-borne sensors and citizen science combine to validate ocean modelling. Scientific Reports, 12, (doi: 10.1038/s41598-022-20254-z)

Garthoff, R., Otto, P. (2022) Spatiotemporal procedures for the statistical surveillance of spatial autoregressive models with heavy tails. Communications in Statistics: Simulation and Computation, 51, pp. 5709-5737. (doi: 10.1080/03610918.2020.1779294)

Browell, J., Gilbert, C., Fasiolo, M. (2022) Covariance structures for high-dimensional energy forecasting. Electric Power Systems Research, 211, (doi: 10.1016/j.epsr.2022.108446)

Otto, P., Schmid, W. (2022) A general framework for spatial GARCH models. Statistical Papers, (doi: 10.1007/s00362-022-01357-1)

Alghamdi, A., Birch, D. J.S., Vyshemirsky, V., Rolinski, O. J. (2022) Impact of the flavonoid quercetin on β-amyloid aggregation revealed by intrinsic fluorescence. Journal of Physical Chemistry B, 126, pp. 7229-7237. (doi: 10.1021/acs.jpcb.2c02763)

Olsen, J. R., Nicholls, N., Caryl, F., Orjuela Mendoza, J., Panis, L. I., Dons, E., Laeremans, M., Standaert, A., Lee, D., Avila-Palencia, I., de Nazelle, A., Nieuwenhuijsen, M., Mitchell, R. (2022) Day-to-day intrapersonal variability in mobility patterns and association with perceived stress: a cross-sectional study using GPS from 122 individuals in three European cities. SSM - Population Health, 19, (doi: 10.1016/j.ssmph.2022.101172)

Alexander, C., Jack, E., McEwan, M., Bock, M. (2022) Exploring Statistics Anxiety in Several Introductory Statistics Courses to Understand Differences Between Types of Students and Types of Courses. (doi: 10.52041/iase.icots11.T14D3)

Low, M., Alexander, C., Bock, M. (2022) Investigating Perceived Student Learning from Peer Review in Statistics Education. (doi: 10.52041/iase.icots11.T14C1)

Castro-Camilo, D., Huser, R., Rue, H. (2022) Practical strategies for GEV-based regression models for extremes. Environmetrics, 33, (doi: 10.1002/env.2742)

Lindgren, K., Bock, M., Alexander, C., Jack, E., Marshall, E. (2022) Statistics Students' Perspectives of Forced Online Learning. (doi: 10.52041/iase.icots11.T14C3)

Elayouty, A., Scott, M., Miller, C. (2022) Time-varying functional principal components for non-stationary EpCO2 in freshwater systems. Journal of Agricultural, Biological, and Environmental Statistics, 27, pp. 506-522. (doi: 10.1007/s13253-022-00494-2)

Heylen, E., Browell, J., Teng, F. (2022) Probabilistic day-ahead inertia forecasting. IEEE Transactions on Power Systems, 37, pp. 3738-3746. (doi: 10.1109/TPWRS.2021.3134811)

Lee, D., Anderson, C. (2022) Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data. Biometrics, (doi: 10.1111/biom.13739)

Lu, Y., Wang, B., Zhao, Y., Yang, X., Li, L., Dong, M., Lv, Q., Zhou, F., Gu, N., Shang, L. (2022) Physics-informed surrogate modeling for hydro-fracture geometry prediction based on deep learning. Energy, 253, (doi: 10.1016/j.energy.2022.124139)

Scott, E.M., Naysmith, P., Cook, G. (2022) What lies behind radiocarbon intercomparisons and the design of the new intercomparison, GIRI? Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, 525, pp. 62-66. (doi: 10.1016/j.nimb.2022.06.015)

Huppmann, D., Browell, J., Nastasi, B., Vale, Z., Süsser, D. (2022) A research agenda for open energy science: Opportunities and perspectives of the F1000Research Energy Gateway. F1000Research, 11, (doi: 10.12688/f1000research.124267.1)

Otto, P., Otto, P. (2022) Impact of academic authorship characteristics on article citations. Revstat Statistical Journal, 20, pp. 427-447. (doi: 10.57805/revstat.v20i4.382)

Szili, B., Niu, M., Neocleous, T. (2022) A Structural Learning Method for Graphical Models. (doi: 10.11159/icsta22.113)

Gong, M., O'Donnell, R., Miller, C., Scott, M., Simis, S., Groom, S., Tyler, A., Hunter, P., Spyrakos, E., Merchant, C., Maberly, S., Carvalho, L. (2022) Adaptive smoothing to identify spatial structure in global lake ecological processes using satellite remote sensing data. Spatial Statistics, 50, (doi: 10.1016/j.spasta.2022.100615)

Lazarus, A., Gao, H., Luo, X., Husmeier, D. (2022) Improving cardio-mechanic inference by combining in vivo strain data with ex vivo volume–pressure data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71, pp. 906-931. (doi: 10.1111/rssc.12560)

Lee, D., Robertson, C., McRae, C., Baker, J. (2022) Quantifying the impact of air pollution on Covid-19 hospitalisation and death rates in Scotland. Spatial and Spatio-Temporal Epidemiology, 42, (doi: 10.1016/j.sste.2022.100523)

Willett, B. J. et al. (2022) SARS-CoV-2 Omicron is an immune escape variant with an altered cell entry pathway. Nature Microbiology, 7, pp. 1161-1179. (doi: 10.1038/s41564-022-01143-7)

Bryce, E., Lombardo, L., van Westen, C., Tanyas, H., Castro-Camilo, D. (2022) Unified landslide hazard assessment using hurdle models: a case study in the Island of Dominica. Stochastic Environmental Research and Risk Assessment, 36, pp. 2071-2084. (doi: 10.1007/s00477-022-02239-6)

Husmeier, D., Dalton, D., Lazarus, A., Gao, H. (2022) Forward and Inverse Uncertainty Quantification in Cardiac Mechanics. (doi: 10.11159/icsta22.161)

Yang, Y., Gao, H., Berry, C., Radjenovic, A., Husmeier, D. (2022) Myocardial Perfusion Classification Using A Markov Random Field Constrained Gaussian Mixture Model. (doi: 10.11159/icsta22.146)

Paun, L. M., Schmidt, A. F., Mcginty, S., Husmeier, D. (2022) Statistical Inference for Optimisation of Drug Delivery from Stents. (doi: 10.11159/icsta22.138)

Al Alawi, M., Ray, S., Gupta, M. (2022) A New Functional Data Clustering Technique Based on Spectral Clustering and Downsampling.

Zhang, W., Ray, S. (2022) Kernel Smoothing-based Probability Contours for Tumour Segmentation.

Vlaar, T., Leimkuhler, B. (2022) Multirate Training of Neural Networks.

Craig, M. T., Wohland, J., Stoop, L. P., Kies, A., Pickering, B., Bloomfield, H. C., Browell, J., De Felice, M., Dent, C. J., Deroubaix, A., Frischmuth, F., Gonzalez, P. L. M., Grochowicz, A., Gruber, K., Härtel, P., Kittel, M., Kotzur, L., Labuhn, I., Lundquist, J. K., Pflugradt, N., van der Wiel, K., Zeyringer, M., Brayshaw, D. J. (2022) Overcoming the disconnect between energy system and climate modeling. Joule, 6, pp. 1405-1417. (doi: 10.1016/j.joule.2022.05.010)

Vlaar, T. J., Frankle, J. (2022) What Can Linear Interpolation of Neural Network Loss Landscapes Tell Us?

Harke, F. H., Merk, M. S., Otto, P. (2022) Estimation of Asymmetric Spatial Autoregressive Dependence on Irregular Lattices. Symmetry, 14, (doi: 10.3390/sym14071474)

Boss, A. N., Banerjee, A., Mamalakis, M., Ray, S., Swift, A., Wilkie, C., Fanstone, J. W., Vorselaars, B., Cole, J., Weeks, S., Mackenzie, L. S. (2022) Development of a mortality prediction model in hospitalised SARS-CoV-2 positive patients based on routine kidney biomarkers. International Journal of Molecular Sciences, 23, (doi: 10.3390/ijms23137260)

Petropoulos, F. et al. (2022) Forecasting: theory and practice. International Journal of Forecasting, 38, pp. 705-871. (doi: 10.1016/j.ijforecast.2021.11.001)

Piter, A., Otto, P., Alkhatib, H. (2022) The Helsinki bike‐sharing system—Insights gained from a spatiotemporal functional model. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185, pp. 1294-1318. (doi: 10.1111/rssa.12834)

Zhang, W., Ray, S. (2022) Analysis of Positron Emission Tomography Data for Tumour Detection and Delineation.

Neethipathi, D., Ganguly, P., Beniwal, A., Scott, M., Bass, A., Dahiya, R. (2022) MoS2 Modified Screen Printed Carbon Electrode Based Flexible Sensor for Detection of Copper. (doi: 10.1109/FLEPS53764.2022.9781564)

White, C. J. et al. (2022) Advances in the application and utility of subseasonal-to-seasonal predictions. Bulletin of the American Meteorological Society, 103, pp. E1448-E1472. (doi: 10.1175/BAMS-D-20-0224.1)

Zhang, H., Swallow, B., Gupta, M. (2022) Bayesian hierarchical mixture models for detecting non-normal clusters applied to noisy genomic and environmental datasets. Australian and New Zealand Journal of Statistics, 64, pp. 313-337. (doi: 10.1111/anzs.12370)

Lee, D., Robertson, C., Marques, D. (2022) Quantifying the small-area spatio-temporal dynamics of the Covid-19 pandemic in Scotland during a period with limited testing capacity. Spatial Statistics, 49, (doi: 10.1016/j.spasta.2021.100508)

Lazarus, A., Dalton, D., Husmeier, D., Gao, H. (2022) Sensitivity analysis and inverse uncertainty quantification for the left ventricular passive mechanics. Biomechanics and Modeling in Mechanobiology, 21, pp. 953-982. (doi: 10.1007/s10237-022-01571-8)

Yin, X., Napier, G., Anderson, C., Lee, D. (2022) Spatio-temporal disease risk estimation using clustering-based adjacency modelling. Statistical Methods in Medical Research, 31, pp. 1184-1203. (doi: 10.1177/09622802221084131)

Fassò, A., Maranzano, P., Otto, P. (2022) Spatiotemporal variable selection and air quality impact assessment of COVID-19 lockdown. Spatial Statistics, 49, (doi: 10.1016/j.spasta.2021.100549)

Tawn, R., Browell, J., McMillan, D. (2022) Subseasonal-to-seasonal forecasting for wind turbine maintenance scheduling. Wind, 2, pp. 260-287. (doi: 10.3390/wind2020015)

Chadwick, F. J., Clark, J., Chowdhury, S., Chowdhury, T., Pascall, D. J., Haddou, Y., Andrecka, J., Kundegorski, M., Wilkie, C., Brum, E., Shirin, T., Alamgir, A.S.M., Rahman, M., Alam, A. N., Khan, F., Swallow, B., Mair, F. S., Illian, J., Trottter, C. L., Hill, D. L., Husmeier, D., Matthiopoulos, J., Hampson, K., Sania, A. (2022) Combining rapid antigen testing and syndromic surveillance improves community-based COVID-19 detection in a low-income country. Nature Communications, 13, (doi: 10.1038/s41467-022-30640-w)

Belmont Osuna, J., Miller, C., Scott, M., Wilkie, C. (2022) A new statistical approach for identifying rare species under imperfect detection. Diversity and Distributions, 28, pp. 882-893. (doi: 10.1111/ddi.13495)

Borowska, A., Gao, H., Lazarus, A., Husmeier, D. (2022) Bayesian optimisation for efficient parameter inference in a cardiac mechanics model of the left ventricle. International Journal for Numerical Methods in Biomedical Engineering, 38, (doi: 10.1002/cnm.3593)

Browell, J., Gilbert, C. (2022) Predicting electricity imbalance prices and volumes: capabilities and opportunities. Energies, 15, (doi: 10.3390/en15103645)

Lavender, E., Aleynik, D., Dodd, J., Illian, J., James, M., Wright, P. J., Smout, S., Thorburn, J. (2022) Behavioural responses of a large, benthic elasmobranch to catch-and-release angling. Frontiers in Marine Science, 9, (doi: 10.3389/fmars.2022.864344)

Cobbold, C. A., Lutscher, F., Yurk, B. (2022) Bridging the scale gap: predicting large-scale population dynamics from small-scale variation in strongly heterogeneous landscapes. Methods in Ecology and Evolution, 13, pp. 866-879. (doi: 10.1111/2041-210X.13799)

Jones, K.A., Small, A.D., Ray, S., Hamilton, D.J., Martin, W., Robinson, J., Goodfield, N.E.R., Paterson, C.A. (2022) Radionuclide ventriculography phase analysis for risk stratification of patients undergoing cardiotoxic cancer therapy. Journal of Nuclear Cardiology, 29, pp. 581-589. (doi: 10.1007/s12350-020-02277-z)

Dawkins, L. C., Osborne, J. M., Economou, T., Darch, G. J.C., Stoner, O. R. (2022) The Advanced Meteorology Explorer: a novel stochastic, gridded daily rainfall generator. Journal of Hydrology, 607, (doi: 10.1016/j.jhydrol.2022.127478)

Wandy, J., Davies, V., McBride, R., Weidt, S., Rogers, S., Daly, R. (2022) ViMMS 2.0: A framework to develop, test and optimise fragmentation strategies in LC-MS metabolomics. Journal of Open Source Software, 7, (doi: 10.21105/joss.03990)

Gerogiannis, G., Tranmer, M., Lee, D., Valente, T. (2022) A Bayesian spatio-network model for multiple adolescent adverse health behaviours. Journal of the Royal Statistical Society: Series C (Applied Statistics), 71, pp. 271-287. (doi: 10.1111/rssc.12531)

Niu, M., Frost, F., Milner, J. E., Skarin, A., Blackwell, P. G. (2022) Modelling group movement with behaviour switching in continuous time. Biometrics, 78, pp. 286-299. (doi: 10.1111/biom.13412)

McLaughlin, K., Hobson, S. R., Ravi Chandran, A., Agarwal, S., Windrim, R. C., Parks, W. T., Bowman, A. W., Sovio, U., Smith, G. C., Kingdom, J. C. (2022) Circulating maternal placental growth factor responses to low molecular weight heparin in pregnant patients at risk of placental dysfunction. American Journal of Obstetrics and Gynecology, 226, pp. S1145-S1156.e1. (doi: 10.1016/j.ajog.2021.08.027)

Paun, L. M., Husmeier, D. (2022) Emulation-accelerated Hamiltonian Monte Carlo algorithms for parameter estimation and uncertainty quantification in differential equation models. Statistics and Computing, 32, (doi: 10.1007/s11222-021-10060-4)

Merk, M. S., Otto, P. (2022) Estimation of the spatial weighting matrix for regular lattice data—An adaptive lasso approach with cross-sectional resampling. Environmetrics, 33, (doi: 10.1002/env.2705)

Lavender, E., Aleynik, D., Dodd, J., Illian, J., James, M., Wright, P. J., Smout, S., Thorburn, J. (2022) Movement patterns of a critically endangered elasmobranch (Dipturus intermedius) in a Marine Protected Area. Aquatic Conservation: Marine and Freshwater Ecosystems, 32, pp. 348-365. (doi: 10.1002/aqc.3753)

Guillot, G., Arab, A., Illian, J. B., Dray, S. (2022) Editorial: advances in statistical ecology: new methods and software. Frontiers in Ecology and Evolution, 9, (doi: 10.3389/fevo.2021.828919)

Zaker, N., Cobbold, C. A., Lutscher, F. (2022) The effect of landscape fragmentation on Turing-pattern formation. Mathematical Biosciences and Engineering, 19, pp. 2506-2537. (doi: 10.3934/mbe.2022116)

Python, A., Bender, A., Blangiardo, M., Illian, J. B., Lin, Y., Liu, B., Lucas, T. C.D., Tan, S., Wen, Y., Svanidze, D., Yin, J. (2022) A downscaling approach to compare COVID-19 count data from databases aggregated at different spatial scales. Journal of the Royal Statistical Society: Series A (Statistics in Society), 185, pp. 202-218. (doi: 10.1111/rssa.12738)

Tawn, R., Browell, J. (2022) A review of very short-term wind and solar power forecasting. Renewable and Sustainable Energy Reviews, 153, (doi: 10.1016/j.rser.2021.111758)

Williamson, L. D., Scott, B. E., Laxton, M. R., Bachl, F. E., Illian, J. B., Brookes, K. L., Thompson, P. M. (2022) Spatiotemporal variation in harbor porpoise distribution and foraging across a landscape of fear. Marine Mammal Science, 38, pp. 42-57. (doi: 10.1111/mms.12839)

Graham, R. M., Browell, J., Bertram, D., White, C. J. (2022) The application of sub‐seasonal to seasonal (S2S) predictions for hydropower forecasting. Meteorological Applications, 29, (doi: 10.1002/met.2047)

Farrokhabadi, M., Browell, J., Wang, Y., Makonin, S., Su, W., Zareipour, H. (2022) Day-ahead electricity demand forecasting competition: post-COVID paradigm. IEEE Open Access Journal of Power and Energy, 9, pp. 185-191. (doi: 10.1109/OAJPE.2022.3161101)

Malinovskaya, A., Otto, P., Peters, T. (2022) Statistical learning for change point and anomaly detection in graphs. Springer

Marshall, E. M., Mahmood, B., Alexander, C., Bock, M., Haigney, D., Jack, E., Verrier, D. (2022) The impact of remote teaching on statistics learning and anxiety. MSOR Connections, 20, pp. 90-101. (doi: 10.21100/msor.v20i1.1312)

Nightingale, G., Laxton, M., Illian, J. B. (2021) How does the community COVID-19 level of risk impact on that of a care home? PLoS ONE, 16, (doi: 10.1371/journal.pone.0260051)

Mamalakis, M., Swift, A. J., Vorselaars, B., Ray, S., Weeks, S., Ding, W., Clayton, R. H., Mackenzie, L. S., Banerjee, A. (2021) DenResCov-19: a deep transfer learning network for robust automatic classification of COVID-19, pneumonia, and tuberculosis from X-rays. Computerized Medical Imaging and Graphics, 94, (doi: 10.1016/j.compmedimag.2021.102008)

Gong, M., Miller, C., O'Donnell, R., Scott, M. (2021) State space functional principal component analysis to identify spatiotemporal patterns in remote sensing lake water quality. Stochastic Environmental Research and Risk Assessment, 35, pp. 2521-2536. (doi: 10.1007/s00477-021-02017-w)

Otto, P., Piter, A., Gijsman, R. (2021) Statistical analysis of beach profiles – A spatiotemporal functional approach. Coastal Engineering, 170, (doi: 10.1016/j.coastaleng.2021.103999)

Garg, N. K., Ray, S., Mathur, A. (2021) Abstract 10932: Prediction of 30-Day Hospital Readmission in High-Risk Atherosclerotic Cardiovascular Disease Patients Using Machine Learning Methods on Electronic Health Record Data from Medical Information Mart for Intensive Care-3 Database. Circulation, 144, (doi: 10.1161/circ.144.suppl_1.10932)

Morrow, A. et al. (2021) Rationale and design of the Medical Research Council Precision medicine with Zibotentan in microvascular angina (PRIZE) trial MRI sub-study. (doi: 10.1136/heartjnl-2021-BSCMR.3)

Lombardo, L., Tanyas, H., Huser, R., Guzzetti, F., Castro-Camilo, D. (2021) Landslide size matters: a new data-driven, spatial prototype. Engineering Geology, 293, (doi: 10.1016/j.enggeo.2021.106288)

Brass, D. P., Cobbold, C. A., Ewing, D. A., Purse, B. V., Callaghan, A., White, S. M. (2021) Phenotypic plasticity as a cause and consequence of population dynamics. Ecology Letters, 24, pp. 2406-2417. (doi: 10.1111/ele.13862)

Browell, J., Fasiolo, M. (2021) Probabilistic forecasting of regional net-load with conditional extremes and gridded NWP. IEEE Transactions on Smart Grid, 12, pp. 5011-5019. (doi: 10.1109/TSG.2021.3107159)

Campioni, N., Husmeier, D., Morales, J., Gaskell, J., Torney, C. J. (2021) Inferring microscale properties of interacting systems from macroscale observations. Physical Review Research, 3, (doi: 10.1103/PhysRevResearch.3.043074)

Muir, R., Forbes, S., Birch, D. J.S., Vyshemirsky, V., Rolinski, O. J. (2021) Collagen glycation detected by its intrinsic fluorescence. Journal of Physical Chemistry B, 125, pp. 11058-11066. (doi: 10.1021/acs.jpcb.1c05001)

Stoner, O., Lewis, J., Martínez, I. L., Gumy, S., Economou, T., Adair-Rohani, H. (2021) Household cooking fuel estimates at global and country level for 1990 to 2030. Nature Communications, 12, (doi: 10.1038/s41467-021-26036-x)

Telford, R., Stephen, B., Browell, J., Haben, S. (2021) Dirichlet sampled capacity and loss estimation for LV distribution networks with partial observability. IEEE Transactions on Power Delivery, 36, pp. 2676-2686. (doi: 10.1109/TPWRD.2020.3025125)

Elliott, A., Law, S., Russell, C. (2021) Explaining Classifiers Using Adversarial Perturbations on the Perceptual Ball. (doi: 10.1109/CVPR46437.2021.01055)

Medina-Lopez, E., McMillan, D., Lazic, J., Hart, E., Zen, S., Angeloudis, A., Bannon, E., Browell, J., Dorling, S., Dorrell, R.M., Forster, R., Old, C., Payne, G.S., Porter, G., Rabaneda, A.S., Sellar, B., Tapoglou, E., Trifonova, N., Woodhouse, I.H., Zampollo, A. (2021) Satellite data for the offshore renewable energy sector: synergies and innovation opportunities. Remote Sensing of Environment, 264, (doi: 10.1016/j.rse.2021.112588)

Testa, B., Reid, J., Scott, M. E., Murison, P. J., Bell, A. (2021) The short form of the Glasgow Composite Measure Pain Scale in post-operative analgesia studies in dogs: a scoping review. Frontiers in Veterinary Science, 8, (doi: 10.3389/fvets.2021.751949)

Scott, E. M., Davies, V., Nolan, A. M., Noble, C. E., Dowgray, N. J., German, A. J., Wiseman-Orr, M. L., Reid, J. (2021) Validity and responsiveness of the generic health-related quality of life instrument (VetMetrica™) in cats with osteoarthritis. Comparison of vet and owner impressions of quality of life impact. Frontiers in Veterinary Science, 8, (doi: 10.3389/fvets.2021.733812)

Manjakkal, L., Mitra, S., Petillot, Y., Shutler, J., Scott, M., Willander, M., Dahiya, R. (2021) Connected sensors, innovative sensor deployment and intelligent data analysis for online water quality monitoring. IEEE Internet of Things Journal, 8, pp. 13805-13824. (doi: 10.1109/JIOT.2021.3081772)

Vandeskog, S. M., Martino, S., Castro-Camilo, D. (2021) Modelling Block Maxima With the Blended Generalised Extreme Value Distribution.

Shu, Q., Scott, M., Todman, L., McGrane, S. J. (2021) Development of a prototype composite index for resilience and security of water-energy-food (WEF) systems in industrialised nations. Environmental and Sustainability Indicators, 11, (doi: 10.1016/j.indic.2021.100124)

Romaszko, L., Borowska, A., Lazarus, A., Dalton, D., Berry, C., Luo, X., Husmeier, D., Gao, H. (2021) Neural network-based left ventricle geometry prediction from CMR images with application in biomechanics. Artificial Intelligence In Medicine, 119, (doi: 10.1016/j.artmed.2021.102140)

Malinovskaya, A., Otto, P. (2021) Online network monitoring. Statistical Methods and Applications, 30, pp. 1337-1364. (doi: 10.1007/s10260-021-00589-z)

Andersson, T. R., Hosking, J. S., Pérez-Ortiz, M., Paige, B., Elliott, A., Russell, C., Law, S., Jones, D. C., Wilkinson, J., Phillips, T., Byrne, J., Tietsche, S., Sarojini, B. B., Blanchard-Wrigglesworth, E., Aksenov, Y., Downie, R., Shuckburgh, E. (2021) Seasonal Arctic sea ice forecasting with probabilistic deep learning. Nature Communications, 12, (doi: 10.1038/s41467-021-25257-4)

Dalton, D., Lazarus, A., Rabbani, A., Gao, H., Husmeier, D. (2021) Graph Neural Network Emulation of Cardiac Mechanics. (doi: 10.11159/icsta21.127)

Paun, L. M., Borowska, A., Colebank, M. J., Olufsen, M. S., Husmeier, D. (2021) Inference in Cardiovascular Modelling Subject to Medical Interventions. (doi: 10.11159/icsta21.109)

Leimkuhler, B., Vlaar, T., Pouchon, T., Storkey, A. (2021) Better Training using Weight-Constrained Stochastic Dynamics.

Wu, J., Gupta, M., Hussein, A. I., Gerstenfeld, L. (2021) Bayesian modeling of factorial time- course data with applications to a bone aging gene expression study. Journal of Applied Statistics, 48, pp. 1730-1754. (doi: 10.1080/02664763.2020.1772733)

Aldossari, S., Husmeier, D., Matthiopoulos, J. (2021) Generalized functional responses in habitat selection fitted by decision trees and random forests. Avestia Publishing

Lee, D., Meeks, K., Pettersson, W. (2021) Improved inference for areal unit count data using graph-based optimisation. Statistics and Computing, 31, (doi: 10.1007/s11222-021-10025-7)

Katina, S., Vittert, L., Bowman, A. W. (2021) Functional data analysis and visualisation of three-dimensional surface shape. Journal of the Royal Statistical Society: Series C (Applied Statistics), 70, pp. 691-713. (doi: 10.1111/rssc.12482)

Zhang, W., Price, S. J., Bonner, S. J. (2021) Maximum likelihood inference for the band-read error model for capture-recapture data with misidentification. Environmental and Ecological Statistics, 28, pp. 405-422. (doi: 10.1007/s10651-021-00492-6)

Ewing, D. A., Purse, B. V., Cobbold, C. A., White, S. M. (2021) A novel approach for predicting risk of vector-borne disease establishment in marginal temperate environments under climate change: West Nile virus in the UK. Journal of the Royal Society: Interface, 18, (doi: 10.1098/rsif.2021.0049)

Davies, V., Wandy, J., Weidt, S., van der Hooft, J. J.J., Miller, A., Daly, R., Rogers, S. (2021) Rapid development of improved data-dependent acquisition strategies. Analytical Chemistry, 93, pp. 5676-5683. (doi: 10.1021/acs.analchem.0c03895)

Wilkie, C., Ray, S., Scott, M., Miller, C. (2021) Bayesian Spatiotemporal Statistical Modelling of Water Quality within Rivers. (doi: 10.5194/egusphere-egu21-10843)

Graham, R. M., Browell, J., Bertram, D., White, C. J. (2021) Developing a Sub-seasonal Forecasting System for Hydropower Reservoirs in Scotland. (doi: 10.5194/egusphere-egu21-7252)

Livingston, M., Pannullo, F., Bowman, A. W., Scott, E. M., Bailey, N. (2021) Exploiting new forms of data to study the private rented sector: strengths and limitations of a database of rental listings. Journal of the Royal Statistical Society: Series A (Statistics in Society), 184, pp. 663-682. (doi: 10.1111/rssa.12643)

Li, X., Yu, L., Yang, X., Ma, Z., Xue, J.-H., Cao, J., Guo, J. (2021) ReMarNet: conjoint relation and margin learning for small-sample image classification. IEEE Transactions on Circuits and Systems for Video Technology, 31, pp. 1569-1579. (doi: 10.1109/TCSVT.2020.3005807)

Wilkie, C., Ray, S., Scott, M., Miller, C., Sinha, R., Bowes, M. (2021) Statistical Downscaling for the Fusion of In-river, Drone and Satellite Water Quality Data in a River Network.

Otto, P., Schmid, W., Garthoff, R. (2021) Stochastic properties of spatial and spatiotemporal ARCH models. Statistical Papers, 62, pp. 623-638. (doi: 10.1007/s00362-019-01106-x)

Borowska, A., Giurghita, D., Husmeier, D. (2021) Gaussian process enhanced semi-automatic approximate Bayesian computation: parameter inference in a stochastic differential equation system for chemotaxis. Journal of Computational Physics, 429, (doi: 10.1016/j.jcp.2020.109999)

Hanlon, P., Chadwick, F., Shah, A., Wood, R., Minton, J., McCartney, G., Fischbacher, C., Mair, F. S., Husmeier, D., Matthiopoulos, J., McAllister, D. A. (2021) COVID-19 – exploring the implications of long-term condition type and extent of multimorbidity on years of life lost: a modelling study. Wellcome Open Research, 5, (doi: 10.12688/wellcomeopenres.15849.3)

Castro-Camilo, D., Mhalla, L., Opitz, T. (2021) Bayesian space-time gap filling for inference on extreme hot-spots: an application to Red Sea surface temperatures. Extremes, 24, pp. 105-128. (doi: 10.1007/s10687-020-00394-z)

Merk, M.S., Otto, P. (2021) Directional spatial autoregressive dependence in the conditional first- and second-order moments. Spatial Statistics, 41, (doi: 10.1016/j.spasta.2020.100490)

Niu, M., Wandy, J., Daly, R., Rogers, S., Husmeier, D. (2021) R package for statistical inference in dynamical systems using kernel based gradient matching: KGode. Computational Statistics, 36, pp. 715-747. (doi: 10.1007/s00180-020-01014-x)

Torney, C. J., Morales, J. M., Husmeier, D. (2021) A hierarchical machine learning framework for the analysis of large scale animal movement data. Movement Ecology, 9, (doi: 10.1186/s40462-021-00242-0)

Rodger, S., Scott, E. M., Nolan, A., Wright, A. K., Reid, J. (2021) Effect of age, breed, and sex on the health-related quality of life of owner assessed healthy dogs. Frontiers in Veterinary Science, 8, (doi: 10.3389/fvets.2021.603139)

Paun, L. M., Husmeier, D. (2021) Markov chain Monte Carlo with Gaussian processes for fast parameter estimation and uncertainty quantification in a 1D fluid‐dynamics model of the pulmonary circulation. International Journal for Numerical Methods in Biomedical Engineering, 37, (doi: 10.1002/cnm.3421)

Davies, V., Reid, J., Scott, E. M. (2021) Optimisation of scores generated by an online feline health–related quality of life (HRQL) instrument to assist the veterinary user interpret its results. Frontiers in Veterinary Science, 7, (doi: 10.3389/fvets.2020.601304)

Milner, J. E., Blackwell, P. G., Niu, M. (2021) Modelling and inference for the movement of interacting animals. Methods in Ecology and Evolution, 12, pp. 54-69. (doi: 10.1111/2041-210X.13468)

Gilbert, C., Browell, J., McMillan, D. (2021) Probabilistic access forecasting for improved offshore operations. International Journal of Forecasting, 37, pp. 134-150. (doi: 10.1016/j.ijforecast.2020.03.007)

Bloomfield, H. C., Gonzalez, P. L. M., Lundquist, J. K., Stoop, L. P., Browell, J., Dargaville, R., De Felice, M., Gruber, K., Hilbers, A., Kies, A., Panteli, M., Thornton, H. E., Wohland, J., Zeyringer, M., Brayshaw, D. J. (2021) The importance of weather and climate to energy systems: a workshop on next generation challenges in energy–climate modeling. Bulletin of the American Meteorological Society, 102, pp. E159-E167. (doi: 10.1175/BAMS-D-20-0256.1)

Mackenzie, L. S., Wilkie, C., Ray, S., Banerjee, A., Mamalakis, M., Swift, A. J., Vorselaars, B., Fanstone, J., Weeks, S. (2021) Can Kidney Function Be Used to Predict Survival of COVID-19 in Hospitals? Predictive Modelling in a Retrospective Cohort Study.

Yang, X., Dong, M., Guo, Y., Xue, J.-H. (2021) Metric Learning for Categorical and Ambiguous Features: An Adversarial Approach. (doi: 10.1007/978-3-030-67661-2_14)

Otto, P. (2021) Parallelized monitoring of dependent spatiotemporal processes. Springer

Antoniuk, A., Merk, M. S., Otto, P. (2021) Spatial statistics, or how to extract knowledge from data. Springer

Enright, J., Lee, D., Meeks, K., Pettersson, W., Sylvester, J. (2021) The complexity of finding optimal subgraphs to represent spatial correlation. Springer

McElarney, Y., Rippey, B., Miller, C., Allen, M., Unwin, A. (2021) The long-term response of lake nutrient and chlorophyll concentrations to changes in nutrient loading in Ireland's largest lake, Lough Neagh. Biology and Environment: Proceedings of the Royal Irish Academy, 121B, pp. 47-60. (doi: 10.3318/bioe.2021.04)

Li, X., Yan, J., Wu, J., Liu, Y., Yang, X., Ma, Z. (2020) Anti-Noise Relation Network for Few-shot Learning.

Nedd, M., Browell, J., Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S., Brush, S. (2020) Operating a Zero Carbon GB Power System in 2025: Frequency and Fault Current [Annexes - Review of System and Network Issues, Frequency Stability, Power Electronic Devices and Fault Current, & Market Needs] (doi: 10.17868/74793)

Stoner, O., Economou, T. (2020) An advanced hidden Markov model for hourly rainfall time series. Computational Statistics and Data Analysis, 152, (doi: 10.1016/j.csda.2020.107045)

Paun, L. M., Colebank, M. J., Olufsen, M. S., Hill, N. A., Husmeier, D. (2020) Assessing model mismatch and model selection in a Bayesian uncertainty quantification analysis of a fluid-dynamics model of pulmonary blood circulation. Journal of the Royal Society: Interface, 17, (doi: 10.1098/rsif.2020.0886)

Redivo, E., Nguyen, H., Gupta, M. (2020) Bayesian clustering of skewed and multimodal data using geometric skewed normal distributions. Computational Statistics and Data Analysis, 152, (doi: 10.1016/j.csda.2020.107040)

Tawn, R., Browell, J., Dinwoodie, I. (2020) Missing data in wind farm time series: properties and effect on forecasts. Electric Power Systems Research, 189, (doi: 10.1016/j.epsr.2020.106640)

Nedd, M., Browell, J., Egea-Alvarez, A., Bell, K., Hamilton, R., Wang, S., Brush, S. (2020) Operating a Zero-Carbon GB Power System: Implications for Scotland. (doi: 10.7488/era/756)

Lee, D., Robertson, C., Ramsay, C., Pyper, K. (2020) Quantifying the impact of the modifiable areal unit problem when estimating the health effects of air pollution. Environmetrics, 31, (doi: 10.1002/env.2643)

Arnold, K. F., Davies, V., de Kamps, M., Tennant, P. W.G., Mbotwa, J., Gilthorpe, M. S. (2020) Reflection on modern methods: generalized linear models for prognosis and intervention—theory, practice and implications for machine learning. International Journal of Epidemiology, 49, pp. 2074-2082. (doi: 10.1093/ije/dyaa049)

Morrow, A. J. et al. (2020) Rationale and design of the Medical Research Council precision medicine with Zibotentan in microvascular angina (PRIZE) trial. American Heart Journal, 229, pp. 70-80. (doi: 10.1016/j.ahj.2020.07.007)

Biosa, G., Giurghita, D., Alladio, E., Vincenti, M., Neocleous, T. (2020) Evaluation of forensic data using logistic regression-based classification methods and an R Shiny implementation. Frontiers in Chemistry, 8, (doi: 10.3389/fchem.2020.00738)

Browell, J., Gilbert, C., Tawn, R., May, L. (2020) Quantile Combination for the EEM20 Wind Power Forecasting Competition. (doi: 10.1109/EEM49802.2020.9221942)

Yang, X., Dong, M., Wang, Z., Gao, L., Zhang, L., Xue, J.-H. (2020) Data-augmented matched subspace detector for hyperspectral subpixel target detection. Pattern Recognition, 106, (doi: 10.1016/j.patcog.2020.107464)

Mittell, E. A., Cobbold, C. A., Ijaz, U. Z., Kilbride, E. A., Moore, K. A., Mable, B. K. (2020) Feral populations of Brassica oleracea along Atlantic coasts in western Europe. Ecology and Evolution, 10, pp. 11810-11825. (doi: 10.1002/ece3.6821)

Romano, E., Diana, A., Miller, C., O'Donnell, R. (2020) Optimally weighted L2 distances for spatially dependent functional data. Spatial Statistics, 39, (doi: 10.1016/j.spasta.2020.100468)

Elliott, A., Chiu, A., Bazzi, M., Reinert, G., Cucuringu, M. (2020) Core–periphery structure in directed networks. Proceedings of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 476, (doi: 10.1098/rspa.2019.0783)

Davies, V., Scott, E. M., Wright, A. K., Reid, J. (2020) Development of an early warning system for owners using a validated health-related quality of life (HRQL) instrument for companion animals and its use in a large cohort of dogs. Frontiers in Veterinary Science, 7, (doi: 10.3389/fvets.2020.575795)

Underwood, W. G., Elliott, A., Cucuringu, M. (2020) Motif-based spectral clustering of weighted directed networks. Applied Network Science, 5, (doi: 10.1007/s41109-020-00293-z)

Browell, J., Gilbert, C. (2020) ProbCast: Open-source Production, Evaluation and Visualisation of Probabilistic Forecasts. (doi: 10.1109/PMAPS47429.2020.9183441)

Stoner, O., Economou, T. (2020) Multivariate hierarchical frameworks for modeling delayed reporting in count data. Biometrics, 76, pp. 789-798. (doi: 10.1111/biom.13188)

Zhang, W., Bonner, S. J. (2020) On continuous‐time capture‐recapture in closed populations. Biometrics, 76, pp. 1028-1033. (doi: 10.1111/biom.13185)

Katina, S., Kelly, B. D., Rojas, M. A., Sukno, F. M., McDermott, A., Hennessy, R. J., Lane, A., Whelan, P. F., Bowman, A. W., Waddington, J. L. (2020) Refining the resolution of craniofacial dysmorphology in bipolar disorder as an index of brain dysmorphogenesis. Psychiatry Research, 291, (doi: 10.1016/j.psychres.2020.113243)

Banerjee, A., Ray, S., Vorselaars, B., Kitson, J., Mamalakis, M., Weeks, S., Baker, M., Mackenzie, L. S. (2020) Use of machine learning and artificial intelligence to predict SARS-CoV-2 infection from full blood counts in a population. International Immunopharmacology, 86, (doi: 10.1016/j.intimp.2020.106705)

Gaskell, J., Campioni, N., Morales, J. M., Husmeier, D., Torney, C. J. (2020) Approximate Bayesian inference for individual-based models with emergent dynamics. Avestia Publishing

Husmeier, D., Paun, L. M. (2020) Closed-loop effects in cardiovascular clinical decision support. Avestia Publishing

Dalton, D., Lazarus, A., Husmeier, D. (2020) Comparative evaluation of different emulators for cardiac mechanics. Avestia Publishing

Campioni, N., Husmeier, D., Morales, J. M., Gaskell, J., Torney, C. J. (2020) Modelling multiscale collective behavior with Gaussian processes. Avestia Publishing

Lee, D. (2020) A tutorial on spatio-temporal disease risk modelling in R using Markov chain Monte Carlo simulation and the CARBayesST package. Spatial and Spatio-Temporal Epidemiology, 34, (doi: 10.1016/j.sste.2020.100353)

Husmeier, D., Paun, L. M. (2020) Closed-loop effects in coupling cardiac physiological models to clinical interventions. Servicio Editorial de la Universidad del País Vasco

Millar, K., Bowman, A. W. (2020) Cognitive archaeology: estimating the effects of blood-lead concentrations on the neuropsychological function of an officer of the 1845 Franklin expedition. Journal of Archaeological Science: Reports, 32, (doi: 10.1016/j.jasrep.2020.102449)

Ray, S., Scott, M., Miller, C. (2020) Developing Statistical Downscaling to Improve Water Quality Understanding and Management in the Ramganga Sub-Basin.

Wacker, L. et al. (2020) Findings from an in-depth annual tree-ring radiocarbon intercomparison. Radiocarbon, 62, pp. 873-882. (doi: 10.1017/RDC.2020.49)

Stoner, O., Shaddick, G., Economou, T., Gumy, S., Lewis, J., Lucio, I., Ruggeri, G., Adair-Rohani, H. (2020) Global household energy model: a multivariate hierarchical approach to estimating trends in the use of polluting and clean fuels for cooking. Journal of the Royal Statistical Society: Series C (Applied Statistics), 69, pp. 815-839. (doi: 10.1111/rssc.12428)

Dalton, D., Husmeier, D. (2020) Improved statistical emulation for a soft-tissue cardiac mechanical model. Servicio Editorial de la Universidad del País Vasco

Borowska, A., Hoogerheide, L., Koopman, S. J., van Dijk, H. K. (2020) Partially censored posterior for robust and efficient risk evaluation. Journal of Econometrics, 217, pp. 335-355. (doi: 10.1016/j.jeconom.2019.12.007)

van der Plicht, J., Bronk Ramsey, C., Heaton, T.J., Scott, E.M., Talamo, S. (2020) Recent developments in calibration for archaeological and environmental samples. Radiocarbon, 62, pp. 1095-1117. (doi: 10.1017/RDC.2020.22)

Aldossari, S., Matthiopoulos, J., Husmeier, D. (2020) Statistical Modelling of Habitat Selection. Servicio Editorial de la Universidad del País Vasco

Reimer, P. J. et al. (2020) The IntCal20 Northern Hemisphere radiocarbon age calibration curve (0-55 kcal BP) Radiocarbon, 62, pp. 725-757. (doi: 10.1017/RDC.2020.41)

Heaton, T.J., Blaauw, M., Blackwell, P.G., Bronk Ramsey, C., Reimer, P.J., Scott, E.M. (2020) The IntCal20 approach to radiocarbon calibration curve construction: a new methodology using Bayesian splines and errors-in-variables. Radiocarbon, 62, pp. 821-863. (doi: 10.1017/RDC.2020.46)

Wilkie, C., Belmont, J., Miller, C., Scott, M., August, T., Taylor, P. (2020) Hierarchical Species Distribution Modelling Across High Dimensional Nested Spatial Scales.

Currie, M., Miller, C., Scott, M., Hills, A. (2020) Sensitivity Analysis Approaches to Investigate Uncertainty in Process-based Models, with Application to Aquaculture.

Milligan, R.J., Scott, E.M., Jones, D.O.B., Bett, B.J., Jamieson, A.J., O'Brien, R., Pereira Costa, S., Rowe, G.T., Ruhl, H.A., Smith Jr., K.L., de Susanne, P., Vardaro, M.F., Bailey, D.M. (2020) Evidence for seasonal cycles in deep-sea fish abundances: A great migration in the deep SE Atlantic? Journal of Animal Ecology, 89, pp. 1593-1603. (doi: 10.1111/1365-2656.13215)

Gilbert, C., Browell, J., McMillan, D. (2020) Leveraging turbine-level data for improved probabilistic wind power forecasting. IEEE Transactions on Sustainable Energy, 11, pp. 1152-1160. (doi: 10.1109/TSTE.2019.2920085)

Dong, M., Wang, Y., Yang, X., Xue, J.-H. (2020) Learning local metrics and influential regions for classification. IEEE Transactions on Pattern Analysis and Machine Intelligence, 42, pp. 1522-1529. (doi: 10.1109/TPAMI.2019.2914899)

Messner, J. W., Pinson, P., Browell, J., Bjerregård, M. B., Schicker, I. (2020) Evaluation of wind power forecasts – an up-to-date view. Wind Energy, 23, pp. 1461-1481. (doi: 10.1002/we.2497)

McKnight, J.A., Ochs, A., Mair, C., McKnight, O., Wright, R., Gibb, F., Cunningham, S.G., Strachan, M., Ritchie, S., McGurnaghan, S.J., Colhoun, H.M. (2020) The effect of DAFNE education, continuous subcutaneous insulin infusion, or both in a population with type 1 diabetes in Scotland. Diabetic Medicine, 37, pp. 1016-1022. (doi: 10.1111/dme.14223)

Li, W., Lazarus, A., Gao, H., Martinez De Azcona Naharro, A., Fontana, M., Hawkins, P., Biswas, S., Janiczek, R., Cox, J., Berry, C., Husmeier, D., Luo, X. (2020) Analysis of cardiac amyloidosis progression using model-based markers. Frontiers in Physiology, 11, (doi: 10.3389/fphys.2020.00324)

Bock, M., Alexander, C., Jack, E., McEwan, M. (2020) Study Design & Quantitative Analysis in Learning and Teaching Scholarship: Case Study Exploring Student Attitudes to Introductory Statistics.

Merk, M. S., Otto, P. (2020) Estimation of anisotropic, time-varying spatial spillovers of fine particulate matter due to wind direction. Geographical Analysis, 52, pp. 254-277. (doi: 10.1111/gean.12205)

Maberly, S. C., O'Donnell, R. A., Woolway, R. I., Cutler, M. E.J., Gong, M., Jones, I. D., Merchant, C. J., Miller, C. A., Politi, E., Scott, E. M., Thackeray, S. J., Tyler, A. N. (2020) Global lake thermal regions shift under climate change. Nature Communications, 11, (doi: 10.1038/s41467-020-15108-z)

Hanlon, P., Quinn, T. J., Gallacher, K. I., Myint, P. K., Jani, B. D., Nicholl, B. I., Lowrie, R., Soiza, R. L., Neal, S. R., Lee, D., Mair, F. S. (2020) Assessing risks of polypharmacy involving medications with anticholinergic properties. Annals of Family Medicine, 18, pp. 148-155. (doi: 10.1370/afm.2501)

Nedd, M., Browell, J., Bell, K., Booth, C. (2020) Containing a credible loss to within frequency stability limits in a low inertia GB power system. IEEE Transactions on Industry Applications, 56, pp. 1031-1039. (doi: 10.1109/TIA.2019.2959996)

Nickbakhsh, S., Mair, C., Matthews, L., Reeve, R., Johnson, P.C.D., Thorburn, F., Von Wissmann, B., McMenamin, J., Gunson, R.N., Murcia, P.R. (2020) Reply to Kloepfer and Gern: Independent studies suggest an arms race between influenza and rhinovirus: what next? Proceedings of the National Academy of Sciences of the United States of America, 117, pp. 6988-6989. (doi: 10.1073/pnas.2000903117)

Sweeney, C., Bessa, R. J., Browell, J., Pinson, P. (2020) The future of forecasting for renewable energy. WIREs Energy and Environment, 9, (doi: 10.1002/wene.365)

Cobbold, C. A., Stana, R. (2020) Should I stay or should I go: partially sedentary populations can outperform fully dispersing populations in response to climate-induced range shifts. Bulletin of Mathematical Biology, 82, (doi: 10.1007/s11538-020-00700-7)

Subbiah, M., Caudell, M. A., Mair, C., Davis, M. A., Matthews, L., Quinlan, R. J., Quinlan, M. B., Lyimo, B., Buza, J., Keyyu, J., Call, D. R. (2020) Antimicrobial resistant enteric bacteria are widely distributed amongst people, animals and the environment in Tanzania. Nature Communications, 11, (doi: 10.1038/s41467-019-13995-5)

Hanlon, P., McCallum, M., Jani, B. D., McQueenie, R., Lee, D., Mair, F. S. (2020) Association between childhood maltreatment and the prevalence and complexity of multimorbidity: a cross-sectional analysis of 157,357 UK Biobank participants. Journal of Comorbidity, 10, pp. 1-12. (doi: 10.1177/2235042X10944344)

Dong, M., Yang, X., Zhu, R., Wang, Y., Xue, J.-H. (2020) Generalization Bound of Gradient Descent for Non-Convex Metric Learning.

Castro-Camilo, D., Huser, R. (2020) Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes. Journal of the American Statistical Association, 115, pp. 1037-1054. (doi: 10.1080/01621459.2019.1647842)

Otto, P. (2019) spGARCH: An R-package for spatial and spatiotemporal ARCH and GARCH models. R Journal, 11, pp. 401-420.

Nickbakhsh, S., Mair, C., Matthews, L., Reeve, R., Johnson, P.C.D., Thorburn, F., Von Wissmann, B., Reynolds, A., McMenamin, J., Gunson, R.N., Murcia, P.R. (2019) Virus-virus interactions impact the population dynamics of influenza and the common cold. Proceedings of the National Academy of Sciences of the United States of America, 116, pp. 27142-27150. (doi: 10.1073/pnas.1911083116)

Mair, C., Nickbakhsh, S., Reeve, R., McMenamin, J., Reynolds, A., Gunson, R. N., Murcia, P. R., Matthews, L. (2019) Estimation of temporal covariances in pathogen dynamics using Bayesian multivariate autoregressive models. PLoS Computational Biology, 15, (doi: 10.1371/journal.pcbi.1007492)

Rolinski, O. J., Alghamdi, A., Wellbrock, T., Birch, D. J.S., Vyshemirsky, V. (2019) Cu2+ effects on beta‐amyloid oligomerisation monitored by fluorescence of intrinsic tyrosine. ChemPhysChem, 20, pp. 3181-3185. (doi: 10.1002/cphc.201900565)

Vittert, L., Bowman, A. W., Katina, S. (2019) A hierarchical curve-based approach to the analysis of manifold data. Annals of Applied Statistics, 13, pp. 2539-2563. (doi: 10.1214/19-AOAS1267)

Leimkuhler, B., Matthews, C., Vlaar, T. (2019) Partitioned integrators for thermodynamic parameterization of neural networks. Foundations of Data Science, 1, pp. 457-489. (doi: 10.3934/fods.2019019)

McGrane, S. J., Acuto, M., Artioli, F., Chen, P.-Y., Comber, R., Cottee, J., Farr-Wharton, G., Green, N., Helfgott, A., Larcom, S., McCann, J. A., O'Reilly, P., Salmoral, G., Scott, M., Todman, L. C., van Gevelt, T., Yan, X. (2019) Scaling the nexus: towards integrated frameworks for analysing water, energy and food. Geographical Journal, 185, pp. 419-431. (doi: 10.1111/geoj.12256)

Buckley, J., Daly, R., Cobbold, C. A., Burgess, K., Mable, B. K. (2019) Changing environments and genetic variation: natural variation in inbreeding does not compromise short-term physiological responses. Proceedings of the Royal Society of London Series B: Biological Sciences, 286, (doi: 10.1098/rspb.2019.2109)

Davies, V., Noè, U., Lazarus, A., Gao, H., Macdonald, B., Berry, C., Luo, X., Husmeier, D. (2019) Fast parameter inference in a biomechanical model of the left ventricle by using statistical emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68, pp. 1555-1576. (doi: 10.1111/rssc.12374)

Edmunds, C., Martín-Martínez, S., Browell, J., Gómez-Lázaro, E., Galloway, S. (2019) On the participation of wind energy in response and reserve markets in Great Britain and Spain. Renewable and Sustainable Energy Reviews, 115, (doi: 10.1016/j.rser.2019.109360)

Browell, J., Stock, A., McMillan, D. (2019) Recommendation for the Evaluation of Wind Farm Power Available Signal Accuracy.

Gilbert, C., Browell, J., McMillan, D. (2019) A Data-driven Vessel Motion Model for Offshore Access Forecasting. (doi: 10.1109/OCEANSE.2019.8867176)

Wandy, J., Davies, V., van der Hooft, J. J.J., Weidt, S., Daly, R., Rogers, S. (2019) In silico optimization of mass spectrometry fragmentation strategies in metabolomics. Metabolites, 9, (doi: 10.3390/metabo9100219)

Amato, G., Eisank, C., Castro-Camilo, D., Lombardo, L. (2019) Accounting for covariate distributions in slope-unit-based landslide susceptibility models. A case study in the alpine environment. Engineering Geology, 260, (doi: 10.1016/j.enggeo.2019.105237)

Colebank, M. J., Paun, L. M., Qureshi, M. U., Chesler, N., Husmeier, D., Olufsen, M. S., Ellwein Fix, L. (2019) Influence of image segmentation on one-dimensional fluid dynamics predictions in the mouse pulmonary arteries. Journal of the Royal Society: Interface, 16, (doi: 10.1098/rsif.2019.0284)

Napier, G., Lee, D., Robertson, C., Lawson, A. (2019) A Bayesian space–time model for clustering areal units based on their disease trends. Biostatistics, 20, pp. 681-697. (doi: 10.1093/biostatistics/kxy024)

Stoner, O., Economou, T., Drummond Marques da Silva, G. (2019) A hierarchical framework for correcting under-reporting in count data. Journal of the American Statistical Association, 114, pp. 1481-1492. (doi: 10.1080/01621459.2019.1573732)

Wu, M., Cai, X., Lin, J., Zhang, X., Scott, E. M., Li, X. (2019) Association between fibre intake and indoxyl sulphate/P-cresyl sulphate in patients with chronic kidney disease: meta-analysis and systematic review of experimental studies. Clinical Nutrition, 38, pp. 2016-2022. (doi: 10.1016/j.clnu.2018.09.015)

Naysmith, P., Scott, E. M., Dunbar, E., Cook, G. T. (2019) Humics - their history in the radiocarbon inter-comparisons studies. Radiocarbon, 61, pp. 1413-1422. (doi: 10.1017/RDC.2019.11)

Scott, E.M., Cook, G.T., Naysmith, P., Staff, R.A. (2019) Learning from the wood samples in ICS, TIRI, FIRI, VIRI and SIRI. Radiocarbon, 61, pp. 1293-1304. (doi: 10.1017/RDC.2019.12)

Scott, E. M., Naysmith, P., Cook, G. T. (2019) Life after SIRI- where next? Radiocarbon, 61, pp. 1159-1168. (doi: 10.1017/RDC.2019.10)

McIntosh, A., Anderson, C. (2019) Modelling the Risk of Exceeding Air Quality Monitoring Thresholds in Scotland.

Richardson, J., Feuchtmayr, H., Miller, C., Hunter, P., Maberly, S. C., Carvalho, L. (2019) The response of cyanobacteria and phytoplankton abundance to warming, extreme rainfall events and nutrient enrichment. Global Change Biology, 25, pp. 3365-3380. (doi: 10.1111/gcb.14701)

Bastos, L. S., Economou, T., Gomes, M. F.C., Villela, D. A.M., Coelho, F. C., Cruz, O. G., Stoner, O., Bailey, T., Codeço, C. T. (2019) A modelling approach for correcting reporting delays in disease surveillance data. Statistics in Medicine, 38, pp. 4363-4377. (doi: 10.1002/sim.8303)

Nightingale, G. F., Illian, J. B., King, R., Nightingale, P. (2019) Area interaction point processes for bivariate point patterns in a Bayesian context. Journal of Environmental Statistics, 9,

Chung, L. H. C., Birch, D. J. S., Vyshemirsky, V., Ryadnov, M. G., Rolinski, O. J. (2019) Tracking insulin glycation in real time by time-resolved emission spectroscopy. Journal of Physical Chemistry B, 123, pp. 7812-7817. (doi: 10.1021/acs.jpcb.9b06363)

Davies, V., Reid, J., Wiseman-Orr, M. L., Scott, E. M. (2019) Optimising outputs from a validated online instrument to measure health-related quality of life (HRQL) in dogs. PLoS ONE, 14, (doi: 10.1371/journal.pone.0221869)

Marinas-Collado, I., Bowman, A., Macaulay, V. (2019) A phylogenetic Gaussian process model for the evolution of curves embedded in d-dimensions. Computational Statistics and Data Analysis, 137, pp. 285-298. (doi: 10.1016/j.csda.2019.03.002)

Castro-Camilo, D., Huser, R., Rue, H. (2019) A spliced Gamma-Generalized Pareto model for short-term extreme wind speed probabilistic forecasting. Journal of Agricultural, Biological and Environmental Statistics, 24, pp. 517-534. (doi: 10.1007/s13253-019-00369-z)

Zhang, W., Bravington, M.V., Fewster, R.M. (2019) Fast likelihood‐based inference for latent count models using the saddlepoint approximation. Biometrics, 75, pp. 723-733. (doi: 10.1111/biom.13030)

Macdonald, B., Husmeier, D. (2019) Model selection via marginal likelihood estimation by combining thermodynamic integration and gradient matching. Statistics and Computing, 29, pp. 853-867. (doi: 10.1007/s11222-018-9840-4)

Grzegorczyk, M., Husmeier, D. (2019) Modelling non-homogeneous dynamic Bayesian networks with piece-wise linear regression models. Wiley

Anderson, C., Hafen, R., Sofrygin, O., Ryan, L. (2019) Comparing predictive abilities of longitudinal child growth models. Statistics in Medicine, 38, pp. 3555-3570. (doi: 10.1002/sim.7693)

Anderson, C., Xiao, L., Checkley, W. (2019) Using data from multiple studies to develop a child growth correlation matrix. Statistics in Medicine, 30, pp. 3540-3554. (doi: 10.1002/sim.7696)

Paun, I., Husmeier, D., Torney, C. (2019) A Study on Discrete-Time Movement Models. (doi: 10.11159/icsta19.27)

Romaszko, L., Borowska, A., Lazarus, A., Gao, H., Luo, X., Husmeier, D. (2019) Direct Learning Left Ventricular Meshes from CMR Images. (doi: 10.11159/icsta19.25)

Paun, L. M., Colebank, M., Qureshi, M. U., Olufsen, M., Hill, N., Husmeier, D. (2019) MCMC with Delayed Acceptance using a Surrogate Model with an Application to Cardiovascular Fluid Dynamics. (doi: 10.11159/icsta19.28)

Romaszko, L., Lazarus, A., Gao, H., Borowska, A., Luo, X., Husmeier, D. (2019) Massive Dimensionality Reduction for the Left Ventricular Mesh. (doi: 10.11159/icsta19.24)

Yang, Y., Gao, H., Berry, C., Radjenovic, A., Husmeier, D. (2019) Quantification of Myocardial Perfusion Lesions Using Spatially Variant Finite Mixture Modelling of DCE-MRI. (doi: 10.11159/icsta19.26)

Hurford, A., Cobbold, C. A., Molnár, P. K. (2019) Skewed temperature dependence affects range and abundance in a warming world. Proceedings of the Royal Society of London Series B: Biological Sciences, 286, (doi: 10.1098/rspb.2019.1157)

Husmeier, D., Lazarus, A., Noè, U., Davies, V., Borowska, A., Macdonald, B., Gao, H., Berry, C., Luo, X. (2019) Statistical Emulation of Cardiac Mechanics: an Important Step Towards a Clinical Decision Support System. (doi: 10.11159/icsta19.29)

Mair, C., Wulaningsih, W., Jeyam, A., McGurnaghan, S., Blackbourn, L., Kennon, B., Leese, G., Lindsay, R., McCrimmon, R. J., McKnight, J., Petrie, J. R., Sattar, N., Wild, S. H., Conway, N., Craigie, I., Robertson, K., Bath, L., McKeigue, P. M., Colhoun, H. M. (2019) Glycaemic control trends in people with type 1 diabetes in Scotland 2004–2016. Diabetologia, 62, pp. 1375-1384. (doi: 10.1007/s00125-019-4900-7)

Davies, V., Harvey, W. T., Reeve, R., Husmeier, D. (2019) Improving the identification of antigenic sites in the H1N1 Influenza virus through accounting for the experimental structure in a sparse hierarchical Bayesian model. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68, pp. 859-885. (doi: 10.1111/rssc.12338)

Devlin, J., Husmeier, D., Mackenzie, J.A. (2019) Optimal estimation of drift and diffusion coefficients in the presence of static localization error. Physical Review E, 100, (doi: 10.1103/PhysRevE.100.022134)

Zhang, W., Liu, J., Goodman, J., Weir, B. S., Fewster, R. M. (2019) Stationary distribution of the linkage disequilibrium coefficient r2. Theoretical Population Biology, 128, pp. 19-26. (doi: 10.1016/j.tpb.2019.05.002)

Python, A., Illian, J. B., Jones‐Todd, C. M., Blangiardo, M. (2019) The deadly facets of terrorism. Significance, 16, pp. 28-31. (doi: 10.1111/j.1740-9713.2019.01300.x)

Noè, U., Lazarus, A., Gao, H., Davies, V., Macdonald, B., Mangion, K., Berry, C., Luo, X., Husmeier, D. (2019) Gaussian process emulation to accelerate parameter estimation in a mechanical model of the left ventricle: a critical step towards clinical end-user relevance. Journal of the Royal Society: Interface, 16, (doi: 10.1098/rsif.2019.0114)

Al Alawi, M., Ray, S., Gupta, M. (2019) A New Framework for Distance-based Functional Clustering.

Yang, X., Zhang, L., Gao, L., Xue, J.-H. (2019) MSDH: matched subspace detector with heterogeneous noise. Pattern Recognition Letters, 125, pp. 701-707. (doi: 10.1016/j.patrec.2019.07.014)

Dean, N., Dong, G., Piekut, A., Pryce, G. (2019) Frontiers in residential segregation: understanding neighbourhood boundaries and their impacts. Tijdschrift voor Economische en Sociale Geografie, 110, pp. 271-288. (doi: 10.1111/tesg.12316)

Niu, M., Cheung, P., Lin, L., Dai, Z., Lawrence, N., Dunson, D. (2019) Intrinsic Gaussian processes on complex constrained domains. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 81, pp. 603-627. (doi: 10.1111/rssb.12320)

Bayarri, M.J., Berger, J. O., Jang, W., Ray, S., Pericchi, L. R., Visser, I. (2019) Prior-based Bayesian information criterion. Statistical Theory and Related Fields, 3, pp. 2-13. (doi: 10.1080/24754269.2019.1582126)

Berger, J., Jang, W., Ray, S., Rericchi, L. R., Visser, I. (2019) Rejoinder by James Berger, Woncheol Jang, Surajit Ray, Luis R. Pericchi and Ingmar Visser. Statistical Theory and Related Fields, 3, pp. 37-39. (doi: 10.1080/24754269.2019.1611147)

Browell, J., Möhrlen, C., Zack, J., Messner, J. W. (2019) IEA Wind Recommended Practices for Selecting Renewable Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions.

Ray, S. (2019) Analysis of PET Imaging for Tumor Delineation.

Stefan, T., Matthews, L., Prada, J. M., Mair, C., Reeve, R., Stear, M. J. (2019) Divergent Allele Advantage provides a quantitative model for maintaining alleles with a wide range of intrinsic merits. Genetics, 212, pp. 553-564. (doi: 10.1534/genetics.119.302022)

Jack, E., Lee, D., Dean, N. (2019) Estimating the changing nature of Scotland's health inequalities using a multivariate spatiotemporal model. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, pp. 1061-1080. (doi: 10.1111/rssa.12447)

Lee, D., Roertson, C., Ramsay, C., Gillespie, C., Napier, G. (2019) Estimating the health impact of air pollution in Scotland, and the resulting benefits of reducing concentrations in city centres. Spatial and Spatio-Temporal Epidemiology, 29, pp. 85-96. (doi: 10.1016/j.sste.2019.02.003)

Mangisa, S., Das, S., Ray, S., Sharp, G. (2019) Functional regression models for South African economic indicators: a growth curve perspective. OPEC Energy Review, 43, pp. 217-237. (doi: 10.1111/opec.12148)

Soriano‐Redondo, A., Jones‐Todd, C. M., Bearhop, S., Hilton, G. M., Lock, L., Stanbury, A., Votier, S. C., Illian, J. B. (2019) Understanding species distribution in dynamic populations: a new approach using spatio‐temporal point process models. Ecography, 42, pp. 1092-1102. (doi: 10.1111/ecog.03771)

Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B. (2019) inlabru: an R package for Bayesian spatial modelling from ecological survey data. Methods in Ecology and Evolution, 10, pp. 760-766. (doi: 10.1111/2041-210X.13168)

Baştürk, N., Borowska, A., Grassi, S., Hoogerheide, L., van Dijk, H.K. (2019) Forecast density combinations of dynamic models and data driven portfolio strategies. Journal of Econometrics, 210, pp. 170-186. (doi: 10.1016/j.jeconom.2018.11.011)

Wilkie, C.J., Miller, C.A., Scott, E.M., O'Donnell, R.A., Hunter, P.D., Spyrakos, E., Tyler, A.N. (2019) Nonparametric statistical downscaling for the fusion of data of different spatiotemporal support. Environmetrics, 30, (doi: 10.1002/env.2549)

Jani, B. D., Hanlon, P., Nicholl, B. I., McQueenie, R., Gallacher, K. I., Lee, D., Mair, F. S. (2019) Relationship between multimorbidity, demographic factors and mortality: findings from the UK Biobank Cohort. BMC Medicine, 17, (doi: 10.1186/s12916-019-1305-x)

Jayaraman, S., Harris, C., Paxton, E., Donachie, A.-M., Vaikkinen, H., McCulloch, R., Hall, J. P.J., Kenny, J., Lenzi, L., Herts-Fowler, C., Cobbold, C., Reeve, R., Micheol, T., Morrison, L. J. (2019) Application of long read sequencing to determine expressed antigen diversity in Trypanosoma brucei infections. PLoS Neglected Tropical Diseases, 13, (doi: 10.1371/journal.pntd.0007262)

Sørbye, S. H., Illian, J. B., Simpson, D. P., Burslem, D., Rue, H. (2019) Careful prior specification avoids incautious inference for log-Gaussian Cox point processes. Journal of the Royal Statistical Society: Series C (Applied Statistics), 68, pp. 543-564. (doi: 10.1111/rssc.12321)

Flynt, A., Dean, N. (2019) Growth mixture modeling with measurement selection. Journal of Classification, 36, pp. 3-25. (doi: 10.1007/s00357-018-9275-9)

Jones-Todd, C. M., Caie, P., Illian, J. B., Stevenson, B. C., Savage, A., Harrison, D. J., Bown, J. L. (2019) Identifying prognostic structural features in tissue sections of colon cancer patients using point pattern analysis. Statistics in Medicine, 38, pp. 1421-1441. (doi: 10.1002/sim.8046)

Tuchow, N. W., Ford, E. B., Papamarkou, T., Lindo, A. (2019) The efficiency of geometric samplers for exoplanet transit timing variation models. Monthly Notices of the Royal Astronomical Society, 484, pp. 3772-3784. (doi: 10.1093/mnras/stz247)

Grau Andres, R., Gray, A., Davies, G. M., Scott, E. M., Waldron, S. (2019) Burning increases post-fire carbon emissions in a heathland and a raised bog, but experimental manipulation of fire severity has no effect. Journal of Environmental Management, 233, pp. 321-328. (doi: 10.1016/j.jenvman.2018.12.036)

Bakka, H., Vanhatalo, J., Illian, J. B., Simpson, D., Rue, H. (2019) Non-stationary Gaussian models with physical barriers. Spatial Statistics, 29, pp. 268-288. (doi: 10.1016/j.spasta.2019.01.002)

Franco-Villoria, M., Scott, M., Hoey, T. (2019) Spatiotemporal modeling of hydrological return levels: A quantile regression approach. Environmetrics, 30, (doi: 10.1002/env.2522)

Flynt, A., Dean, N., Nugent, R. (2019) sARI: a soft agreement measure for class partitions incorporating assignment probabilities. Advances in Data Analysis and Classification, 13, pp. 303-323. (doi: 10.1007/s11634-018-0346-x)

Möhrlen, C., Zack, J., Lerner, J., Messner, J., Browell, J., Collier, C., Tuohy, A., Sharp, J., Westenholz, M. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 2: Designing and Executing Forecasting Benchmarks and Trials.

Möhrlen, C., Zack, J., Messner, J., Browell, J., Collier, C. (2019) Recommended Practices for the Implementation of Wind Power Forecasting Solutions: Part 3: Evaluation of Forecasts and Forecast Solutions.

McLean, M.I., Evers, L., Bowman, A.W., Bonte, M., Jones, W.R. (2019) Statistical modelling of groundwater contamination monitoring data: a comparison of spatial and spatiotemporal methods. Science of the Total Environment, 652, pp. 1339-1346. (doi: 10.1016/j.scitotenv.2018.10.231)

Macaulay, V., Soares, P., Richards, M. B. (2019) Rectifying long-standing misconceptions about the p statistic for molecular dating. PLoS ONE, 14, (doi: 10.1371/journal.pone.0212311)

Finazzi, F., Napier, Y., Scott, M., Hills, A., Cameletti, M. (2019) A statistical emulator for multivariate model outputs with missing values. Atmospheric Environment, 199, pp. 415-422. (doi: 10.1016/j.atmosenv.2018.11.025)

Ewing, D. A., Cobbold, C. A., Purse, B. V., Schafer, S. M., White, S. M. (2019) Uncovering mechanisms behind mosquito seasonality by integrating mathematical models and daily empirical population data: Culex pipiens in the UK. Parasites and Vectors, 12, (doi: 10.1186/s13071-019-3321-2)

Beresford, N.A., Scott, E. M., Copplestone, D. (2019) Field effects studies in the Chernobyl Exclusion Zone: lessons to be learnt. Journal of Environmental Radioactivity, (doi: 10.1016/j.jenvrad.2019.01.005)

Grau-Andrés, R., Davies, G. M., Waldron, S., Scott, E. M., Gray, A. (2019) Increased fire severity alters initial vegetation regeneration across Calluna-dominated ecosystems. Journal of Environmental Management, 231, pp. 1004-1011. (doi: 10.1016/j.jenvman.2018.10.113)

Bowman, A. W. (2019) Graphics for uncertainty. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, pp. 403-418. (doi: 10.1111/rssa.12379)

Umar Qureshi, M., Colebank, M. J., Paun, L. M., Ellwein, L., Chesler, N., Haider, M. A., Hill, N. A., Husmeier, D., Olufsen, M. S. (2019) Hemodynamic assessment of pulmonary hypertension in mice: a model based analysis of the disease mechanism. Biomechanics and Modeling in Mechanobiology, 18, pp. 219-243. (doi: 10.1007/s10237-018-1078-8)

Borowska, A., Hoogerheide, L. F., Koopman, S. J. (2019) Bayesian Risk Forecasting for Long Horizons. (doi: 10.2139/ssrn.3339819)

Python, A., Illian, J. B., Jones‐Todd, C. M., Blangiardo, M. (2019) A Bayesian approach to modelling subnational spatial dynamics of worldwide non‐state terrorism, 2010–2016. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182, pp. 323-344. (doi: 10.1111/rssa.12384)

Pennino, M. G., Paradinas, I., Illian, J. B., Muñoz, F., Bellido, J. M., López-Quílez, A., Conesa, D. (2019) Accounting for preferential sampling in species distribution models. Ecology and Evolution, 9, pp. 653-663. (doi: 10.1002/ece3.4789)

Moesta, A., Hours, M.A., Boutigny, L., Davies, V., Kelly, S., Mougeotc, I., Reid, J. (2019) A Weight Loss Diet With Alpha-casozepine and L-tryptophane Improves Health Related Quality of Life in Dogs.

Noble, C. E., Wiseman-Orr, L. M., Scott, M. E., Nolan, A. M., Reid, J. (2019) Development, initial validation and reliability testing of a web-based, generic feline health-related quality of life instrument. Journal of Feline Medicine and Surgery, 21, pp. 84-94. (doi: 10.1177/1098612X18758176)

Lin, L., Niu, M., Cheung, P., Dunson, D. (2019) Extrinsic Gaussian processes for regression and classification on manifolds. Bayesian Analysis, 14, pp. 887-906. (doi: 10.1214/18-BA1135)

Otto, P. (2019) Modeling Spatial Dependence in Local Risks and Uncertainties.

Grzegorczyk, M., Aderhold, A., Husmeier, D. (2019) Overview and evaluation of recent methods for statistical inference of gene regulatory networks from time series data. Humana Press

Otto, P. (2019) Parallelized Monitoring of Dependent Spatiotemporal Processes.

Illian, J. B. (2019) Spatial and spatio-temporal point processes in ecological applications. CRC Press

Low, M.I., Bonte, M., Evers, L., Bowman, A.W., Jones, W.R. (2019) Statistical Modelling of Groundwater Contamination Monitoring Data using GWSDAT: a Comparison of Spatial and Spatiotemporal Methods.

Lee, D. (2018) A locally adaptive process-convolution model for estimating the health impact of air pollution. Annals of Applied Statistics, 12, pp. 2540-2558. (doi: 10.1214/18-AOAS1167)

Krainski, E. T., Gómez-Rubio, V., Bakka, H., Lenzi, A., Castro-Camilo, D., Simpson, D., Lindgren, F., Rue, H. (2018) Advanced Spatial Modeling With Stochastic Partial Differential Equations Using R and INLA. Chapman & Hall/CRC

Vittert, L., Katina, S., Ayoub, A., Khambay, B., Bowman, A.W. (2018) Assessing the outcome of orthognathic surgery by three-dimensional soft tissue analysis. International Journal of Oral and Maxillofacial Surgery, 47, pp. 1587-1595. (doi: 10.1016/j.ijom.2018.05.024)

Hildeman, A., Bolin, D., Wallin, J., Illian, J. B. (2018) Level set Cox processes. Spatial Statistics, 28, pp. 169-193. (doi: 10.1016/j.spasta.2018.03.004)

Richardson, J., Miller, C., Maberly, S., Taylor, P., Globevnik, L., Hunter, P., Jeppesen, E., Mischke, U., Moe, J., Pasztaleniec, A., Søndergaard, M., Carvalho, L. (2018) Effects of multiple stressors on cyanobacteria abundance varies with lake type. Global Change Biology, 24, pp. 5044-5055. (doi: 10.1111/gcb.14396)

Caudell, M. A., Mair, C., Subbiah, M., Matthews, L., Quinlan, R. J., Zadoks, R., Keyyu, J., Call, D. R. (2018) Identification of risk factors associated with carriage of resistant Escherichia coli in three culturally diverse ethnic groups in Tanzania: a biological and socioeconomic analysis. Lancet Planetary Health, 2, pp. e489-e497. (doi: 10.1016/S2542-5196(18)30225-0)

Browell, J., Drew, D.R., Philippopoulos, K. (2018) Improved very-short-term wind forecasting using atmospheric regimes. Wind Energy, 21, pp. 968-979. (doi: 10.1002/we.2207)

Jani, B. D., Nicholl, B. I., McQueenie, R., Connelly, D. T., Hanlon, P., Gallacher, K. I., Lee, D., Mair, F. S. (2018) Multimorbidity and co-morbidity in atrial fibrillation and effects on survival: findings from UK Biobank cohort. Europace, 20, pp. f329-f336. (doi: 10.1093/europace/eux322)

Bakka, H., Rue, H., Fuglstad, G.-A., Riebler, A., Bolin, D., Illian, J., Krainski, E., Simpson, D., Lindgren, F. (2018) Spatial modeling with R-INLA: A review. Wiley Interdisciplinary Reviews: Computational Statistics, 10, (doi: 10.1002/wics.1443)

Möhrlen, C., Lerner, J., Messner, J. W., Browell, J., Tuohy, A., Zack, J., Collier, C., Giebel, G. (2018) IEA Wind Recommended Practices for the Implementation of Wind Power Forecasting Solutions Part 2 and 3: Designing and Executing Forecasting Benchmarks and Evaluation of Forecast Solutions.

Young, D. M., Parry, L. E., Lee, D., Ray, S. (2018) Spatial models with covariates improve estimates of peat depth in blanket peatlands. PLoS ONE, 13, (doi: 10.1371/journal.pone.0202691)

Bakka, H. C., Castro-Camilo, D., Franco-Villoria, M., Freni-Sterrantino, A., Huser, T., Rue, H. (2018) Contributed discussion of "Using Stacking to Average Bayesian Predictive Distributions" by Yao et. al​. Bayesian Analysis, 13, pp. 982-985. (doi: 10.1214/17-BA1091)

Hoolohan, C., Larkin, A., McLachlan, C., Falconer, R., Soutar, I., Suckling, J., Varga, L., Haltas, I., Druckman, A., Lumbroso, D., Scott, M., Gilmour, D., Ledbetter, R., McGrane, S., Mitchell, C., Yu, D. (2018) Engaging stakeholders in research to address water–energy–food (WEF) nexus challenges. Sustainability Science, 13, pp. 1415-1426. (doi: 10.1007/s11625-018-0552-7)

Gilbert, C., Browell, J., McMillan, D. (2018) A Hierarchical Approach to Probabilistic Wind Power Forecasting. (doi: 10.1109/PMAPS.2018.8440571)

Cullen, B., Newby, D., Lee, D., Lyall, D. M., Nevado-Holgado, A. J., Evans, J. J., Pell, J. P., Lovestone, S., Cavanagh, J. (2018) Cross-sectional and longitudinal analyses of outdoor air pollution exposure and cognitive function in UK Biobank. Scientific Reports, 8, (doi: 10.1038/s41598-018-30568-6)

Yu, C., Zhang, W., Xu, X., Ji, Y., Yu, S. (2018) Data mining based multi-level aggregate service planning for cloud manufacturing. Journal of Intelligent Manufacturing, 29, pp. 1351-1361. (doi: 10.1007/s10845-015-1184-8)

Păun, L. M., Qureshi, M. U., Colebank, M., Hill, N. A., Olufsen, M. S., Haider, M. A., Husmeier, D. (2018) MCMC methods for inference in a mathematical model of pulmonary circulation. Statistica Neerlandica, 72, pp. 306-338. (doi: 10.1111/stan.12132)

Giurghita, D., Husmeier, D. (2018) Statistical modelling of cell movement. Statistica Neerlandica, 72, pp. 265-280. (doi: 10.1111/stan.12140)

Otto, P., Schmid, W. (2018) Discussion of “Statistical methods for network surveillance” by Daniel Jeske, Nathaniel Stevens, Alexander Tartakovsky, and James Wilson. Applied Stochastic Models in Business and Industry, 34, pp. 452-456. (doi: 10.1002/asmb.2360)

Hanlon, P., Nicholl, B. I., Jani, B. D., Lee, D., McQueenie, R., Mair, F. S. (2018) Frailty and pre-frailty in middle-aged and older adults and its association with multimorbidity and mortality: a prospective analysis of 493,737 UK Biobank participants. Lancet Public Health, 3, pp. e323-e332. (doi: 10.1016/S2468-2667(18)30091-4)

Elayouty, A., Scott, M., Miller, C., Waldron, S. (2018) Functional Principal Component Analysis for Non-stationary Dynamic Time Series.

Wandy, J., Niu, M., Giurghita, D., Daly, R., Rogers, S., Husmeier, D. (2018) ShinyKGode: an interactive application for ODE parameter inference using gradient matching. Bioinformatics, 34, pp. 2314-2315. (doi: 10.1093/bioinformatics/bty089)

Gong, M., Miller, C., Scott, M. (2018) Spatio-temporal Modelling of Remote-sensing Lake Surface Water Temperature Data.

Wilkie, C., Miller, C., Scott, M., Simis, S., Groom, S., Hunter, P., Spyrakos, E., Tyler, A. (2018) Spatiotemporal Statistical Downscaling for the Fusion of In-lake and Remote Sensing Data.

Barra, I., Borowska, A., Koopman, S. J. (2018) Bayesian dynamic modeling of high-frequency integer price changes. Journal of Financial Econometrics, 16, pp. 384-424. (doi: 10.1093/jjfinec/nby010)

Browell, J. (2018) Risk constrained trading strategies for stochastic generation with a single-price balancing market. Energies, 11, (doi: 10.3390/en11061345)

Reid, J., Wiseman-Orr, L., Scott, M. (2018) Shortening of an existing generic online health-related quality of life instrument for dogs. Journal of Small Animal Practice, 59, pp. 334-342. (doi: 10.1111/jsap.12772)

Niu, M., Macdonald, B., Rogers, S., Filippone, M., Husmeier, D. (2018) Statistical inference in mechanistic models: time warping for improved gradient matching. Computational Statistics, 33, pp. 1091-1123. (doi: 10.1007/s00180-017-0753-z)

Garthoff, R., Otto, P. (2018) Verfahren zur Überwachung räumlicher autoregressiver Prozesse mit externen Regressoren = Statistical surveillance of spatial autoregressive processes with exogenous regressors. AStA Wirtschafts- und Sozialstatistisches Archiv, 12, pp. 107-133. (doi: 10.1007/s11943-018-0224-1)

Bowman, A. W. (2018) Big questions, informative data, excellent science. Statistics and Probability Letters, 136, pp. 34-36. (doi: 10.1016/j.spl.2018.02.017)

Chanialidis, C., Evers, L., Neocleous, T., Nobile, A. (2018) Efficient Bayesian inference for COM-Poisson regression models. Statistics and Computing, 28, pp. 595-608. (doi: 10.1007/s11222-017-9750-x)

Lindo, A., Zuyev, S., Sagitov, S. (2018) Nonparametric estimation for compound Poisson process via variational analysis on measures. Statistics and Computing, 28, pp. 563-577. (doi: 10.1007/s11222-017-9748-4)

Huque, M. H., Anderson, C., Walton, R., Woolford, S., Ryan, L. (2018) Smooth individual level covariates adjustment in disease mapping. Biometrical Journal, 60, pp. 597-615. (doi: 10.1002/bimj.201700143)

Scott, E. M. (2018) The role of Statistics in the era of big data: crucial, critical and under-valued. Statistics and Probability Letters, 136, pp. 20-24. (doi: 10.1016/j.spl.2018.02.050)

Jones-Todd, C. M., Swallow, B., Illian, J. B., Toms, M. (2018) A spatiotemporal multispecies model of a semicontinuous response. Journal of the Royal Statistical Society: Series C (Applied Statistics), 67, pp. 705-722. (doi: 10.1111/rssc.12250)

Mangion, K., Gao, H., Husmeier, D., Luo, X., Berry, C. (2018) Advances in computational modelling for personalised medicine after myocardial infarction. Heart, 104, pp. 550-557. (doi: 10.1136/heartjnl-2017-311449)

Lee, D., Rushworth, A., Napier, G. (2018) Spatio-temporal areal unit modelling in R with conditional autoregressive priors using the CARBayesST package. Journal of Statistical Software, 84, (doi: 10.18637/jss.v084.i09)

Huang, G., Lee, D., Scott, E. M. (2018) Multivariate space-time modelling of multiple air pollutants and their health effects accounting for exposure uncertainty. Statistics in Medicine, 37, pp. 1134-1148. (doi: 10.1002/sim.7570)

Grau-Andres, R., Davies, G. M., Gray, A., Scott, E. M., Waldron, S. (2018) Fire severity is more sensitive to low fuel moisture content on Calluna heathlands than on peat bogs. Science of the Total Environment, 616-17, pp. 1261-1269. (doi: 10.1016/j.scitotenv.2017.10.192)

Spyrakos, E. et al. (2018) Optical types of inland and coastal waters. Limnology and Oceanography, 63, pp. 846-870. (doi: 10.1002/lno.10674)

Castro-Camilo, D., de Carvalho, M., Wadsworth, J. (2018) Time-varying extreme value dependence with application to leading European stock markets. Annals of Applied Statistics, 12, pp. 283-309. (doi: 10.1214/17-AOAS1089)

Gallacher, K. I., McQueenie, R., Nicholl, B., Jani, B. D., Lee, D., Mair, F. S. (2018) Risk factors and mortality associated with multimorbidity in people with stroke or transient ischaemic attack: a study of 8,751 UK Biobank participants. Journal of Comorbidity, 8, pp. 1-8. (doi: 10.15256/joc.2018.8.129)

Scott, E. M., Naysmith, P., Cook, G. T. (2018) Why do we need 14C inter-comparisons?: The Glasgow 14C inter-comparison series, a reflection over 30 years. Quaternary Geochronology, 43, pp. 72-82. (doi: 10.1016/j.quageo.2017.08.001)

Alghamdi, A., Vyshemirsky, V., Birch, D.J.S., Rolinski, O.J. (2018) Detecting beta-amyloid aggregation from the time-resolved emission spectra. Methods and Applications in Fluorescence, 6, (doi: 10.1088/2050-6120/aa9f95)

Elliott, A., Leicht, E., Whitmore, A., Reinert, G., Reed-Tsochas, F. (2018) A nonparametric significance test for sampled networks. Bioinformatics, 34, pp. 64-71. (doi: 10.1093/bioinformatics/btx419)

Hanlon, P., Nicholl, B. I., Jani, B. D., McQueenie, R., Lee, D., Gallacher, K. I., Mair, F. S. (2018) Examining patterns of multimorbidity, polypharmacy and risk of adverse drug reactions in chronic obstructive pulmonary disease: a cross-sectional UK Biobank study. BMJ Open, 8, (doi: 10.1136/bmjopen-2017-018404)

Lindo, A., Sagitov, S. (2018) General linear-fractional branching processes with discrete time. Stochastics, 90, pp. 364-378. (doi: 10.1080/17442508.2017.1357722)

Yurk, B. P., Cobbold, C. A. (2018) Homogenization techniques for population dynamics in strongly heterogeneous landscapes. Journal of Biological Dynamics, 12, pp. 171-193. (doi: 10.1080/17513758.2017.1410238)

Lazarus, A., Husmeier, D., Papamarkou, T. (2018) Multiphase MCMC sampling for parameter inference in nonlinear ordinary differential equations. Proceedings of Machine Learning Research, 84, pp. 1252-1260.

Chung, L. H. C., Birch, D. J.S., Vyshemirsky, V., Ryadnov, M. G., Rolinski, O. J. (2017) Insulin aggregation tracked by its intrinsic TRES. Applied Physics Letters, 111, (doi: 10.1063/1.5008477)

McCulloch, R., Cobbold, C. A., Figueiredo, L., Jackson, A., Morrison, L. J., Mugnier, M. R., Papavasiliou, N., Schnaufer, A., Matthews, K. (2017) Emerging challenges in understanding trypanosome antigenic variation. Emerging Topics in Life Sciences, 1, pp. 585-592. (doi: 10.1042/ETLS20170104)

Grau-Andrés, R., Davies, G. M., Waldron, S., Scott, E. M., Gray, A. (2017) Leaving moss and litter layers undisturbed reduces the short-term environmental consequences of heathland managed burns. Journal of Environmental Management, 204, pp. 102-110. (doi: 10.1016/j.jenvman.2017.08.017)

Yuan, Y., Bachl, F. E., Lindgren, F., Borchers, D. L., Illian, J. B., Buckland, S. T., Rue, H., Gerrodette, T. (2017) Point process models for spatio-temporal distance sampling data from a large-scale survey of blue whales. Annals of Applied Statistics, 11, pp. 2270-2297. (doi: 10.1214/17-AOAS1078)

Waddington, J. L., Katina, S., O'Tuathaigh, C. M.P., Bowman, A. W. (2017) Translational genetic modelling of 3D craniofacial dysmorphology: elaborating the facial phenotype of neurodevelopmental disorders through the prism of schizophrenia. Current Behavioral Neuroscience Reports, 4, pp. 322-330. (doi: 10.1007/s40473-017-0136-3)

Neslihanoglu, S., Sogiakas, V., McColl, J., Lee, D. (2017) Nonlinearities in the CAPM: evidence from developed and emerging markets. Journal of Forecasting, 36, pp. 867-897. (doi: 10.1002/for.2389)

Milotti, E., Vyshemirsky, V., Stella, S., Dogo, F., Chignola, R. (2017) Analysis of the fluctuations of the tumour/host interface. Physica A: Statistical Mechanics and its Applications, 486, pp. 587-594. (doi: 10.1016/j.physa.2017.06.005)

Liu, C., Ray, S., Hooker, G. (2017) Functional principal component analysis of spatially correlated data. Statistics and Computing, 27, pp. 1639-1654. (doi: 10.1007/s11222-016-9708-4)

Castro-Camilo, D., Lombardo, L., Mai, P. M., Dou, J., Huser, R. (2017) Handling high predictor dimensionality in slope-unit-based landslide susceptibility models through LASSO-penalized Generalized Linear Model. Environmental Modelling and Software, 97, pp. 145-156. (doi: 10.1016/j.envsoft.2017.08.003)

Reid, J., Scott, M., Nolan, A. (2017) Pain assessment in companion animals: an update. In Practice, 39, pp. 446-451. (doi: 10.1136/inp.j4513)

Gao, H., Mangion, K., Carrick, D., Husmeier, D., Luo, X., Berry, C. (2017) Estimating prognosis in patients with acute myocardial infarction using personalized computational heart models. Scientific Reports, 7, (doi: 10.1038/s41598-017-13635-2)

Illian, J. B., Burslem, D. F.R.P. (2017) Improving the usability of spatial point process methodology: an interdisciplinary dialogue between statistics and ecology. AStA Advances in Statistical Analysis, 101, pp. 495-520. (doi: 10.1007/s10182-017-0301-8)

Dunbar, E., Naysmith, P., Cook, G.T., Scott, E.M., Xu, S., Tripney, B.G. (2017) Investigation of the analytical F14C bone background value at SUERC. Radiocarbon, 59, pp. 1463-1473. (doi: 10.1017/RDC.2017.67)

Dean, N., Pryce, G. (2017) Is the housing market blind to religion? A perceived substitutability approach to homophily and social integration. Urban Studies, 54, pp. 3058-3070. (doi: 10.1177/0042098016668779)

Naysmith, P., Dunbar, E., Scott, E.M., Cook, G.T., Tripney, B.G. (2017) Preliminary results for estimating the bone background uncertainties at SUERC using statistical analysis. Radiocarbon, 59, pp. 1579-1587. (doi: 10.1017/RDC.2017.70)

Scott, E.M., Naysmith, P., Cook, G.T. (2017) Should archaeologists care about 14C inter-comparisons? Why? A summary report on SIRI. Radiocarbon, 59, pp. 1589-1596. (doi: 10.1017/RDC.2017.12)

Lazarus, A., Husmeier, D., Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling with Gradient Matching.

Pasetto, M., Noè, U., Luati, A., Husmeier, D. (2017) Inference on the Duffing System With the Unscented Kalman Filter and Optimization of Sigma Points.

Niu, M., Rogers, S., Filippone, M., Husmeier, D. (2017) Parameter Inference in Differential Equation Models Using Time Warped Gradient Matching.

Paun, L., Haider, M., Hill, N., Olufsen, M., Qureshi, M., Papamarkou, T., Husmeier, D. (2017) Parameter Inference in the Pulmonary Blood Circulation.

Stewart, K., Matthews, L., Scott, E. M., Husmeier, D., Mccowan, C. (2017) Preliminary Investigation of the Influences on Antimicrobial Resistance.

Husmeier, D., Ferguson, E., Matthiopoulos, J., Insall, R. (2017) Statistical Inference of the Drivers of Collective Cell Movement.

Giurghita, D., Husmeier, D. (2017) Statistical Modelling of Cell Movement Data Using the Unscented Kalman Filter.

Castro-Camilo, D., de Carvalho, M. (2017) Spectral density regression for bivariate extremes. Stochastic Environmental Research and Risk Assessment, 31, pp. 1603-1613. (doi: 10.1007/s00477-016-1257-z)

Davies, V., Reeve, R., Harvey, W. T., Maree, F. F., Husmeier, D. (2017) A sparse hierarchical Bayesian model for detecting relevant antigenic sites in virus evolution. Computational Statistics, 32, pp. 803-843. (doi: 10.1007/s00180-017-0730-6)

Che Roos, N. A., Alsanosi, S. M., Alsieni, M. A., Gupta, M., Padmanabhan, S. (2017) Antihypertensive Drugs and Risk of Cancer: A Systematic Review and Meta-Analysis of 391, 790 Patients. (doi: 10.1161/hyp.70.suppl_1.p129)

Bessa, R., Möhrlen, C., Fundel, V., Siefert, M., Browell, J., Haglund El Gaidi, S., Hodge, B.-M., Cali, U., Kariniotakis, G. (2017) Towards improved understanding of the applicability of uncertainty forecasts in the electric power industry. Energies, 10, (doi: 10.3390/en10091402)

Malvaldi, A., Weiss, S., Infield, D., Browell, J., Leahy, P., Foley, A.M. (2017) A spatial and temporal correlation analysis of aggregate wind power in an ideally interconnected Europe. Wind Energy, 20, pp. 1315-1329. (doi: 10.1002/we.2095)

Barraquand, F., Louca, S., Abbott, K. C., Cobbold, C. A., Cordoleani, F., DeAngelis, D. L., Elderd, B. D., Fox, J. W., Greenwood, P., Hilker, F. M., Murray, D. L., Stieha, C. R., Taylor, R. A., Vitense, K., Wolkowicz, G. S.K., Tyson, R. C. (2017) Moving forward in circles: challenges and opportunities in modelling population cycles. Ecology Letters, 20, pp. 1074-1092. (doi: 10.1111/ele.12789)

Ferguson, E. A., Matthiopoulos, J., Insall, R. H., Husmeier, D. (2017) Statistical inference of the mechanisms driving collective cell movement. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66, pp. 869-890. (doi: 10.1111/rssc.12203)

Browell, J., Gilbert, C. (2017) Cluster-based Regime-switching AR for the EEM 2017 Wind Power Forecasting Competition. (doi: 10.1109/EEM.2017.7982034)

Alexander, C., Stuart-Smith, J., Neocleous, T., Evers, L. (2017) Using Chain Graph Models for Structural Inference With an Application to Linguistic Data.

Otto, P. (2017) A note on efficient simulation of multidimensional spatial autoregressive processes. Communications in Statistics: Simulation and Computation, 46, pp. 4547-4558. (doi: 10.1080/03610918.2015.1122050)

Ferguson, E. A., Matthiopoulos, J., Husmeier, D. (2017) Constructing Wildebeest Density Distributions by Spatio-temporal Smoothing of Ordinal Categorical Data Using GAMs.

Lazarus, A., Husmeier, D., Papamarkou, T. (2017) Inference in Complex Systems Using Multi-Phase MCMC Sampling With Gradient Matching Burn-in.

Paun, L. M., Qureshi, M. U., Colebank, M., Haider, M. A., Olufsen, M. S., Hill, N. A., Husmeier, D. (2017) Parameter Inference in the Pulmonary Circulation of Mice.

Pasetto, M. E., Husmeier, D., Noè, U., Luati, A. (2017) Statistical Inference in the Duffing System with the Unscented Kalman Filter.

Giurghita, D., Husmeier, D. (2017) Statistical Modelling of Cell Movement.

Aderhold, A., Husmeier, D., Grzegorczyk, M. (2017) Approximate Bayesian inference in semi-mechanistic models. Statistics and Computing, 27, pp. 1003-1040. (doi: 10.1007/s11222-016-9668-8)

Gao, H., Aderhold, A., Mangion, K., Luo, X., Husmeier, D., Berry, C. (2017) Changes and classification in myocardial contractile function in the left ventricle following acute myocardial infarction. Journal of the Royal Society: Interface, 14, (doi: 10.1098/rsif.2017.0203)

Bush, A. et al. (2017) Connecting Earth observation to high-throughput biodiversity data. Nature Ecology and Evolution, 1, (doi: 10.1038/s41559-017-0176)

Grzegorczyk, M., Aderhold, A., Husmeier, D. (2017) Targeting Bayes factors with direct-path non-equilibrium thermodynamic integration. Computational Statistics, 32, pp. 717-761. (doi: 10.1007/s00180-017-0721-7)

Reid, J., Scott, E.M., Calvo, G., Nolan, A.M. (2017) Definitive Glasgow Acute Pain Scale for Cats: validation and intervention level. Veterinary Record, 108, (doi: 10.1136/vr.104208)

Yamaoka, T. T., Flaherty, D., Pawson, P., Scott, M., Auckburally, A. (2017) Comparison of arterial blood pressure measurements obtained invasively or oscillometrically using a Datex S/5 Compact monitor in anaesthetised adult horses. Veterinary Anaesthesia and Analgesia, 44, pp. 492-501. (doi: 10.1016/j.vaa.2016.05.008)

Olivieri, A. et al. (2017) Mitogenome diversity in Sardinians: a genetic window onto an island's past. Molecular Biology and Evolution, 34, pp. 1230-1239. (doi: 10.1093/molbev/msx082)

Papáček, Š., Matonoha, C., Macdonald, B. (2017) Closed-form formulae vs. PDE based numerical solution for the FRAP data processing: Theoretical and practical comparison. Computers and Mathematics with Applications, 73, pp. 1673-1683. (doi: 10.1016/j.camwa.2017.02.010)

Lee, D., Mukhopadhyay, S., Rushworth, A., Sahu, S. K. (2017) A rigorous statistical framework for spatio-temporal pollution prediction and estimation of its long-term impact on health. Biostatistics, 18, pp. 370-385. (doi: 10.1093/biostatistics/kxw048)

Cavalcante, L., Bessa, R. J., Reis, M., Browell, J. (2017) LASSO vector autoregression structures for very short-term wind power forecasting. Wind Energy, 20, pp. 657-675. (doi: 10.1002/we.2029)

Labrosse, N., Bownes, J., Forrest, D., MacTaggart, D., McGookin, E., Poet, R., Ray, S., Fischbacher-Smith, M., Jackson, M., McEwan, M., Pringle Barnes, G., Sheridan, N. (2017) Preparing for the Journey: Supporting Students to Make Successful Transitions Into and Out of Taught Postgraduate Study.

Pannullo, F., Lee, D., Neal, L., Dalvi, M., Agnew, P., O'Connor, F. M., Mukhopadhyay, S., Sahu, S., Sarran, C. (2017) Quantifying the impact of current and future concentrations of air pollutants on respiratory disease risk in England. Environmental Health, 16, (doi: 10.1186/s12940-017-0237-1)

Rue, H., Riebler, A., Sørbye, S. H., Illian, J. B., Simpson, D. P., Lindgren, F. K. (2017) Bayesian computing with INLA: a review. Annual Review of Statistics and Its Application, 4, pp. 395-421. (doi: 10.1146/annurev-statistics-060116-054045)

Gallacher, K., Miller, C., Scott, E.M., Willows, R., Pope, L., Douglass, J. (2017) Flow-directed PCA for monitoring networks. Environmetrics, 28, (doi: 10.1002/env.2434)

Anderson, C., Ryan, L. (2017) A comparison of spatio-temporal disease mapping approaches including an application to ischaemic heart disease in New South Wales, Australia. International Journal of Environmental Research and Public Health, 14, (doi: 10.3390/ijerph14020146)

Otto, P., Lange, A.-L. (2017) Arbeitsbuch der Angewandten Statistik: Mit Aufgaben zur Software R und detaillierten Lösungen. Springer Gabler

Rushworth, A., Lee, D., Sarran, C. (2017) An adaptive spatio-temporal smoothing model for estimating trends and step changes in disease risk. Journal of the Royal Statistical Society: Series C (Applied Statistics), 66, pp. 141-157. (doi: 10.1111/rssc.12155)

Anderson, C., Lee, D., Dean, N. (2017) Spatial clustering of average risks and risk trends in Bayesian disease mapping. Biometrical Journal, 59, pp. 41-56. (doi: 10.1002/bimj.201600018)

Millar, K., Bowman, A. W. (2017) “Hartnell's time machine” reprise: Further implications of zinc, lead and copper in the thumbnail of a Franklin expedition crewmember. Journal of Archaeological Science: Reports, 13, pp. 286-290. (doi: 10.1016/j.jasrep.2017.03.046)

Lawson, A., Lee, D. (2017) Bayesian disease mapping for public health. Elsevier

Liu, Z., Macdonald, B., Husmeier, D., Giurghita, D. (2017) Estimating Parameters of Partial Differential Equations with Gradient Matching.

Python, A., Illian, J., Jones-Todd, C., Blangiardo, M. (2017) Explaining the lethality of Boko Haram’s terrorist attacks in Nigeria, 2009–2014: a hierarchical Bayesian approach. Springer

Noè, U., Chen, W. W., Filippone, M., Hill, N., Husmeier, D. (2017) Inference in a Partial Differential Equations Model of Pulmonary Arterial and Venous Blood Circulation using Statistical Emulation. (doi: 10.1007/978-3-319-67834-4_15)

McMillan, D., Browell, J. (2017) Optimisation of Wind Energy O&M Decision Making Under Uncertainty [Final Report]: Exploitation Plan.

Niu, M., Rogers, S., Filippone, M., Husmeier, D. (2017) Parameter Inference in Differential Equation Models of Biopathways using Time Warped Gradient Matching. (doi: 10.1007/978-3-319-67834-4_12)

Browell, J., Gilbert, C., McMillan, D. (2017) Use of Turbine-level Data for Improved Wind Power Forecasting. (doi: 10.1109/PTC.2017.7981134)

Lange, A.-L., Otto, P. (2016) Bayes’sche Statistik in der Dienstleistungsforschung. AStA Wirtschafts- und Sozialstatistisches Archiv, 10, pp. 247-267. (doi: 10.1007/s11943-016-0189-x)

Ledo, A., Cornulier, T., Illian, J. B., Iida, Y., Kassim, A. R., Burslem, D. F. R. P. (2016) Re-evaluation of individual diameter: height allometric models to improve biomass estimation of tropical trees. Ecological Applications, 26, pp. 2376-2382. (doi: 10.1002/eap.1450)

Ventrucci, M., Cocchi, D., Scott, E. M. (2016) Smoothing of land use maps for trend and change detection in urbanization. Evironmental and Ecological Statistics, 23, pp. 565-584. (doi: 10.1007/s10651-016-0354-y)

Dowell, J., Hawker, G., Bell, K., Gill, S. (2016) A Review of Probabilistic Methods for Defining Reserve Requirements. (doi: 10.1109/PESGM.2016.7741361)

Ledo, A., Illian, J. B., Schnitzer, S. A., Wright, S. J., Dalling, J. W., Burslem, D. F. R. P., Zotz, G. (2016) Lianas and soil nutrients predict fine-scale distribution of above-ground biomass in a tropical moist forest. Journal of Ecology, 104, pp. 1819-1828. (doi: 10.1111/1365-2745.12635)

Chojnacki, L., Cook, C. Q., Dalidovich, D., Hayward Sierens, L. E., Lantagne-Hurtubise, É., Melko, R. G., Vlaar, T. J. (2016) Shape dependence of two-cylinder Rényi entropies for free bosons on a lattice. Physical Review B, 94, (doi: 10.1103/PhysRevB.94.165136)

Ferguson, E. A., Matthiopoulos, J., Insall, R. H., Husmeier, D. (2016) Inference of the drivers of collective movement in two cell types: Dictyostelium and melanoma. Journal of the Royal Society: Interface, 13, (doi: 10.1098/rsif.2016.0695)

Otto, P., Schmid, W. (2016) Spatiotemporal analysis of German real-estate prices. Annals of Regional Science, 60, pp. 41-72. (doi: 10.1007/s00168-016-0789-y)

Lee, D., Lawson, A. (2016) Quantifying the spatial inequality and temporal trends in maternal smoking rates in Glasgow. Annals of Applied Statistics, 10, pp. 1427-1446. (doi: 10.1214/16-AOAS941)

Sagitov, S., Lindo, A. (2016) A special family of Galton-Watson processes with explosions. Springer

Minghella, E., Auckburally, A., Pawson, P., Scott, M. E., Flaherty, D. (2016) Clinical effects of midazolam or lidocaine co-induction with a propofol target-controlled infusion (TCI) in dogs. Veterinary Anaesthesia and Analgesia, 43, pp. 472-481. (doi: 10.1111/vaa.12336)

Otto, P., Schmid, W. (2016) Detection of spatial change points in the mean and covariances of multivariate simultaneous autoregressive models. Biometrical Journal, 58, pp. 1113-1137. (doi: 10.1002/bimj.201500148)

Browell, J., Dinwoodie, I., McMillan, D. (2016) Forecasting for Day-ahead Offshore Maintenance Scheduling Under Uncertainty. (doi: 10.1201/9781315374987-16)

Martyna, A., Zadora, G., Neocleous, T., Michalska, A., Dean, N. (2016) Hybrid approach combining chemometrics and likelihood ratio framework for reporting the evidential value of spectra. Analytica Chimica Acta, 931, pp. 34-46. (doi: 10.1016/j.aca.2016.05.016)

Giurghita, D., Husmeier, D. (2016) Inference in Nonlinear Systems with Unscented Kalman Filters.

Mavrogonatou, L., Vyshemirsky, V. (2016) Sequential Importance Sampling for Online Bayesian Changepoint Detection.

Napier, G., Lee, D., Robertson, C., Lawson, A., Pollock, K. G. (2016) A model to estimate the impact of changes in MMR vaccine uptake on inequalities in measles susceptibility in Scotland. Statistical Methods in Medical Research, 25, pp. 1185-1200. (doi: 10.1177/0962280216660420)

Lawson, A. B., Lee, D., MacNab, Y. (2016) Editorial. Statistical Methods in Medical Research, 25, pp. 1079. (doi: 10.1177/0962280216660410)

Pannullo, F., Lee, D., Waclawski, E., Leyland, A. H. (2016) How robust are the estimated effects of air pollution on health? Accounting for model uncertainty using Bayesian model averaging. Spatial and Spatio-Temporal Epidemiology, 18, pp. 53-62. (doi: 10.1016/j.sste.2016.04.001)

Huque, M. H., Anderson, C., Walton, R., Ryan, L. (2016) Individual level covariate adjusted conditional autoregressive (indiCAR) model for disease mapping. International Journal of Health Geographics, 15, (doi: 10.1186/s12942-016-0055-7)

Garthoff, R., Otto, P. (2016) Control charts for multivariate spatial autoregressive models. AStA Advances in Statistical Analysis, 101, pp. 67-94. (doi: 10.1007/s10182-016-0276-x)

Ewing, D. A., Cobbold, C. A., Nunn, M. A., Purse, B. V., White, S. M. (2016) Modelling the effect of temperature on the seasonal population dynamics of temperate mosquitoes. Journal of Theoretical Biology, 400, pp. 65-79. (doi: 10.1016/j.jtbi.2016.04.008)

Macdonald, B., Niu, M., Rogers, S., Filippone, M., Husmeier, D. (2016) Approximate parameter inference in systems biology using gradient matching: a comparative evaluation. BioMedical Engineering OnLine, 15, (doi: 10.1186/s12938-016-0186-x)

Bessa, R. J., Dowell, J., Pinson, P. (2016) Renewable energy forecasting. John Wiley and Sons Ltd

Millar, K., Bowman, A. W., Battersby, W., Welbury, R. R. (2016) The health of nine Royal Naval Arctic crews, 1848 to 1854: implications for the lost Franklin Expedition. Polar Record, 52, pp. 423-441. (doi: 10.1017/S0032247416000176)

Browell, J. (2016) Forecasting Electricity Prices and Market Length for Trading Stochastic Generation in Markets With a Single-price Balancing Mechanism.

Niu, M., Rogers, S., Filippone, M., Husmeier, D. (2016) Fast inference in nonlinear dynamical systems using gradient matching. Proceedings of Machine Learning Research, 48, pp. 1699-1707.

Niu, M., Blackwell, P. G., Skarin, A. (2016) Modeling interdependent animal movement in continuous time. Biometrics, 72, pp. 315-324. (doi: 10.1111/biom.12454)

Rolinski, O. J., McLaughlin, D., Birch, D. J.S., Vyshemirsky, V. (2016) Resolving environmental microheterogeneity and dielectric relaxation in fluorescence kinetics of protein. Methods and Applications in Fluorescence, 4, (doi: 10.1088/2050-6120/4/2/024001)

Flynt, A., Dean, N. (2016) A survey of popular R packages for cluster analysis. Journal of Educational and Behavioral Statistics, 41, pp. 205-225. (doi: 10.3102/1076998616631743)

Cai, X., Li, Z., Scott, E. M., Li, X., Tang, M. (2016) Short-term effects of atmospheric particulate matter on myocardial infarction: a cumulative meta-analysis. Environmental Science and Pollution Research, 7, pp. 6139-6148. (doi: 10.1007/s11356-016-6186-3)

Aderhold, A., Smith, V. A., Husmeier, D. (2016) Biological network inference at multiple scales: from gene regulation to species interactions. John Wiley & Sons

Elayouty, A., Scott, M., Miller, C., Waldron, S., Franco-Villoria, M. (2016) Challenges in modeling detailed and complex environmental data sets: a case study modeling the excess partial pressure of fluvial CO2. Evironmental and Ecological Statistics, 23, pp. 65-87. (doi: 10.1007/s10651-015-0329-4)

Simpson, D., Illian, J.B., Lindgren, F., Sørbye, S.H., Rue, H. (2016) Going off grid: computationally efficient inference for log-Gaussian Cox processes. Biometrika, 103, pp. 49-70. (doi: 10.1093/biomet/asv064)

Soares, P. J., Trejaut, J. A., Rito, T., Cavadas, B., Hill, C., Eng, K. K., Mormina, M., Brandao, A., Fraser, R. M., Wang, T.-Y., Loo, J.-H., Snell, C., Ko, T.-M., Amorim, A., Pala, M., Macaulay, V., Bulbeck, D., Wilson, J. F., Gusmao, L., Pereira, L., Oppenheimer, S., Lin, M., Richards, M. B. (2016) Resolving the ancestry of Austronesian-speaking populations. Human Genetics, 135, pp. 309-326. (doi: 10.1007/s00439-015-1620-z)

Katina, S., McNeil, K., Ayoub, A., Guilfoyle, B., Khambay, B., Siebert, J., Sukno, F., Rojas, M., Vittert, L., Waddington, J., Whelan, P. F., Bowman, A. (2016) The definitions of three-dimensional landmarks on the human face: an interdisciplinary view. Journal of Anatomy, 228, pp. 355-365. (doi: 10.1111/joa.12407)

Dowell, J., Pinson, P. (2016) Very-short-term probabilistic wind power forecasts by sparse vector autoregression. IEEE Transactions on Smart Grid, 7, pp. 763-770. (doi: 10.1109/TSG.2015.2424078)

Altieri, L., Cocchi, D., Greco, F., Illian, J.B., Scott, E.M. (2016) Bayesian P-splines and advanced computing in R for a changepoint analysis on spatio-temporal point processes. Journal of Statistical Computation and Simulation, 86, pp. 2531-2545. (doi: 10.1080/00949655.2016.1146280)

Catterson, V.M., McMillan, D., Dinwoodie, I., Revie, M., Dowell, J., Quigley, J., Wilson, K. (2016) An economic impact metric for evaluating wave height forecasters for offshore wind maintenance access. Wind Energy, 19, pp. 199-212. (doi: 10.1002/we.1826)

Anderson, C., Lee, D., Dean, N. (2016) Bayesian cluster detection via adjacency modelling. Spatial and Spatio-Temporal Epidemiology, 16, pp. 11-20. (doi: 10.1016/j.sste.2015.11.005)

Blackwell, P. G., Niu, M., Lambert, M. S., LaPoint, S. D., O'Hara, R. B. (2016) Exact Bayesian inference for animal movement in continuous time. Methods in Ecology and Evolution, 7, pp. 184-195. (doi: 10.1111/2041-210X.12460)

Brown, C., Illian, J. B., Burslem, D. F. R. P., Sandhu, L. (2016) Success of spatial statistics in determining underlying process in simulated plant communities. Journal of Ecology, 104, pp. 160-172. (doi: 10.1111/1365-2745.12493)

Lee, D., Sahu, S. (2016) Estimating the health impact of air pollution fields. CRC

Chignola, R., Sega, M., Stella, S., Vyshemirsky, V., Milotti, E. (2016) From single-cell dynamics to scaling laws in oncology. World Scientific

Reeve, R., Leinster, T., Cobbold, C. A., Thompson, J., Brummitt, N., Mitchell, S. N., Matthews, L. (2016) How to partition diversity. arXiv,

Kavanagh, L., Lee, D., Pryce, G. (2016) Is poverty decentralizing? Quantifying uncertainty in the decentralization of urban poverty. Annals of the American Association of Geographers, 106, pp. 1286-1298. (doi: 10.1080/24694452.2016.1213156)

Davies, V., Reeve, R., Harvey, W. T., Husmeier, D. (2016) Selecting random effect components in a sparse hierarchical Bayesian model for identifying antigenic variability. Springer

Lindo, A., Sagitov, S. (2015) Asymptotic results for the number of Wagner’s solutions to a generalised birthday problem. Statistics and Probability Letters, 107, pp. 356-361. (doi: 10.1016/j.spl.2015.09.014)

Cobbold, C. A., Lutscher, F., Sherratt, J. A. (2015) Diffusion-driven instabilities and emerging spatial patterns in patchy landscapes. Ecological Complexity, 24, pp. 69-81. (doi: 10.1016/j.ecocom.2015.10.001)

Malvaldi, A., Dowell, J., Weiss, S., Infield, D. (2015) Wind Prediction Enhancement by Exploiting Data Non-stationarity. (doi: 10.1049/cp.2015.1795)

Macdonald, B., Husmeier, D. (2015) Gradient matching methods for computational inference in mechanistic models for systems biology: a review and comparative analysis. Frontiers in Bioengineering and Biotechnology, 3, (doi: 10.3389/fbioe.2015.00180)

Altieri, L., Scott, E. M., Cocchi, D., Illian, J. B. (2015) A changepoint analysis of spatio-temporal point processes. Spatial Statistics, 14, pp. 197-207. (doi: 10.1016/j.spasta.2015.05.005)

Dunlop, K.M., Ruxton, G.D., Scott, E.M., Bailey, D.M. (2015) Absolute abundance estimates from shallow water baited underwater camera surveys; a stochastic modelling approach tested against field data. Journal of Experimental Marine Biology and Ecology, 472, pp. 126-134. (doi: 10.1016/j.jembe.2015.07.010)

Lee, D., Sarran, C. (2015) Controlling for unmeasured confounding and spatial misalignment in long-term air pollution and health studies. Environmetrics, 26, pp. 477-487. (doi: 10.1002/env.2348)

Fitzer, S. C., Vittert, L., Bowman, A., Kamenos, N. A., Phoenix, V. R., Cusack, M. (2015) Ocean acidification and temperature increase impacts mussel shell shape and thickness: problematic for protection? Ecology and Evolution, 5, pp. 4875-4884. (doi: 10.1002/ece3.1756)

Russell, N., Cook, G. T., Ascough, P. L., Scott, E. M. (2015) A period of calm in Scottish seas: a comprehensive study of ΔR values for the northern British Isles coast and the consequent implications for archaeology and oceanography. Quaternary Geochronology, 30, pp. 34-41. (doi: 10.1016/j.quageo.2015.08.001)

Huang, G., Lee, D., Scott, M. (2015) An integrated Bayesian model for estimating the long-term health effects of air pollution by fusing modelled and measured pollution data: a case study of nitrogen dioxide concentrations in Scotland. Spatial and Spatio-Temporal Epidemiology, 14-15, pp. 63-74. (doi: 10.1016/j.sste.2015.09.002)

Pannullo, F., Lee, D., Waclawski, E., Leyland, A. H. (2015) Improving spatial nitrogen dioxide prediction using diffusion tubes: a case study in West Central Scotland. Atmospheric Environment, 118, pp. 227-235. (doi: 10.1016/j.atmosenv.2015.08.009)

Dunlop, K. M., Scott, E. M., Parsons, D., Bailey, D. M. (2015) Do agonistic behaviours bias baited remote underwater video surveys of fish? Marine Ecology, 36, pp. 810-818. (doi: 10.1111/maec.12185)

Evers, L., Molinari, D.A., Bowman, A.W., Jones, W.R., Spence, M.J. (2015) Efficient and automatic methods for flexible regression on spatiotemporal data, with applications to groundwater monitoring. Environmetrics, 26, pp. 431-441. (doi: 10.1002/env.2347)

Nightingale, G. F., Illian, J. B., King, R. (2015) Pairwise interaction point processes for modelling bivariate spatial point patterns in the presence of interaction uncertainty. Journal of Environmental Statistics, 7,

Napier, G., Nobile, A., Neocleous, T. (2015) An online application for the classification and evidence evaluation of forensic glass fragments. Chemometrics and Intelligent Laboratory Systems, 146, pp. 418-425. (doi: 10.1016/j.chemolab.2015.06.013)

Rolinski, O. J., Wellbrock, T., Birch, D. J.S., Vyshemirsky, V. (2015) Tyrosine photophysics during the early stages of β-amyloid aggregation leading to Alzheimer’s. Journal of Physical Chemistry Letters, 6, pp. 3116-3120. (doi: 10.1021/acs.jpclett.5b01285)

Ponomarev, D., Miller, C., Govan, L., Haig, C., Wu, O., Langhorne, P. (2015) Complications following incident stroke resulting in readmissions: an analysis of data from three Scottish health surveys. International Journal of Stroke, 10, pp. 911-917. (doi: 10.1111/ijs.12191)

Cobbold, C. A., Teng, J., Muldowney, J. S. (2015) The influence of host competition and predation on tick densities and management implications. Theoretical Ecology, 8, pp. 349-368. (doi: 10.1007/s12080-015-0255-y)

Rushworth, A.M., Peterson, E.E., Ver Hoef, J.M., Bowman, A.W. (2015) Validation and comparison of geostatistical and spline models for spatial stream networks. Environmetrics, 25, pp. 327-338. (doi: 10.1002/env.2340)

O'Donnell, R., Miller, C., Scott, E. (2015) Within Lake Clustering of High Resolution Satellite Retrievals: A Functional Data and Clustering Approach.

Macdonald, B., Higham, C., Husmeier, D. (2015) Controversy in mechanistic modelling with Gaussian processes. Proceedings of Machine Learning Research, 37, pp. 1539-1547.

Noè, U., Filippone, M., Husmeier, D. (2015) Emulation of ODEs with Gaussian Processes.

Niu, M., Filippone, M., Husmeier, D., Rogers, S. (2015) Inference in Nonlinear Differential Equations.

Grzegorczyk, M., Aderhold, A., Husmeier, D. (2015) Network Reconstruction with Realistic Models.

Wilkie, C. J., Scott, E. M., Miller, C., Tyler, A. N., Hunter, P. D., Spyrakos, E. (2015) Data Fusion of Remote-sensing and In-lake chlorophylla Data Using Statistical Downscaling. (doi: 10.1016/j.proenv.2015.05.014)

Gong, M., Miller, C., Scott, E. (2015) Functional PCA for Remotely Sensed Lake Surface Water Temperature Data. (doi: 10.1016/j.proenv.2015.05.015)

Bowman, A. W., Katina, S., Smith, J., Brown, D. (2015) Anatomical curve identification. Computational Statistics and Data Analysis, 86, pp. 52-64. (doi: 10.1016/j.csda.2014.12.007)

Millar, K., Bowman, A. W., Battersby, W. (2015) A re-analysis of the supposed role of lead poisoning in Sir John Franklin’s last expedition, 1845-1848. Polar Record, 51, pp. 224-238. (doi: 10.1017/S0032247413000867)

Mair, C., Matthews, L., Prada J. de Cisneros, J., Stefan, T., Stear, M. J. (2015) Multitrait indices to predict worm length and number in sheep with natural, mixed predominantly Teladorsagia circumcincta infection. Parasitology, 142, pp. 773-782. (doi: 10.1017/S0031182014001905)

Hughes, J. S., Cobbold, C. A., Haynes, K., Dwyer, G. (2015) The effects of forest spatial structure on insect outbreak dynamics: insights from host-parasitoid models. American Naturalist, 183, pp. E130-E152. (doi: 10.1086/680860)

Bagchi, R., Illian, J. B., Murrell, D. (2015) A method for analysing replicated point patterns in ecology. Methods in Ecology and Evolution, 6, pp. 482-490. (doi: 10.1111/2041-210X.12335)

Cook, G.T., Ascough, P.L., Bonsall, C., Hamilton, W.D., Russell, N., Sayle, K.L., Scott, E.M., Bownes, J.M. (2015) Best practice methodology for 14C calibration of marine and mixed terrestrial/marine samples. Quaternary Geochronology, 27, pp. 164-171. (doi: 10.1016/j.quageo.2015.02.024)

Macdonald, B., Husmeier, D. (2015) Computational inference in systems biology. Springer

Grzegorczyk, M., Aderhold, A., Husmeier, D. (2015) Inferring bi-directional interactions between circadian clock genes and metabolism with model ensembles. Statistical Applications in Genetics and Molecular Biology, 14, pp. 143-167. (doi: 10.1515/sagmb-2014-0041)

O'Donnell, R.A., Miller, C.A., Scott, E.M. (2015) Spatially weighted functional clustering of river network data. Journal of the Royal Statistical Society: Series C (Applied Statistics), 64, pp. 491-506. (doi: 10.1111/rssc.12082)

Moser, C. B., Gupta, M., Archer, B. N., White, L. F. (2015) The impact of prior information on estimates of disease transmissibility using Bayesian tools. PLoS ONE, 10, (doi: 10.1371/journal.pone.0118762)

Prasad, S., Katina, S., Hennessy, R. J., Murphy, K. C., Bowman, A. W., Waddington, J. L. (2015) Craniofacial dysmorphology in 22q11.2 deletion syndrome by 3D laser surface imaging and geometric morphometrics: illuminating the developmental relationship to risk for psychosis. American Journal of Medical Genetics Part A, 167, pp. 529-536. (doi: 10.1002/ajmg.a.36893)

Garthoff, R., Otto, P. (2015) Simultaneous surveillance of means and covariances of spatial models. Springer

Finazzi, F., Haggarty, R., Miller, C., Scott, M., Fassò, A. (2015) A comparison of clustering approaches for the study of the temporal coherence of multiple time series. Stochastic Environmental Research and Risk Assessment, 29, pp. 463-475. (doi: 10.1007/s00477-014-0931-2)

Napier, G., Neocleous, T., Nobile, A. (2015) A composite Bayesian hierarchical model of compositional data with zeros. Journal of Chemometrics, 9, pp. 96-108. (doi: 10.1002/cem.2681)

Lee, D., Minton, J., Pryce, G. (2015) Bayesian inference for the dissimilarity index in the presence of spatial autocorrelation. Spatial Statistics, 11, pp. 81-95. (doi: 10.1016/j.spasta.2014.12.001)

Jull, A.J.T., Scott, E.M., Bierman, P. (2015) The CRONUS-Earth inter-comparison for cosmogenic isotope analysis. Quaternary Geochronology, 26, pp. 3-10. (doi: 10.1016/j.quageo.2013.09.003)

Mair, C., Stear, M., Johnson, P., Denwood, M., Jimenez de Cisneros, J. P., Stefan, T., Matthews, L. (2015) A Bayesian generalized random regression model for estimating heritability using overdispersed count data. Genetics Selection Evolution, 47, (doi: 10.1186/s12711-015-0125-5)

Reid, J., Wiseman-Orr, M. L., Nolan, A., Scott, E. M. (2015) Health-related quality of life measurement. Elsevier

Higham, C. F., Husmeier, D. (2015) Inference of circadian regulatory pathways based on delay differential equations. Springer

Dowell, J., Weiss, S., Infield, D. (2015) Kernel Methods for Short-term Spatio-temporal Wind Prediction. (doi: 10.1109/PESGM.2015.7285965)

Smith, E., Miller, C., McConway, K., Merrill, S., Opsomer, J., Zacks, S., Sloan, R., House, L. (2015) Statistics for the Curious: Why Study Statistics? Curious Academic Publishing

Heydtmann, M., Macdonald, B., Lewsey, J., Masson, N., Cunningham, L., Irnazarow, A., Nardone, A., Cosgrave, J., Chick, J. (2015) Tailored dose baclofen in patients with alcoholic liver disease: a case series with 2-year follow-up of hospitalisation. Addiction Research and Theory, 23, pp. 510-517. (doi: 10.3109/16066359.2015.1040003)