Statistics

Miller, C., Scott, M. (2025) Improving national water quality monitoring and network design through statistical methods. Springer Cham

Belbe, Ș., Moldovan, D., Andrieș, A. M., Otto, P., Mare, C. (2024) Evaluation of the space-time effects of Covid-19 on household loans and savings in Romania - A spatial panel data approach at county level. Emerging Markets Review, 63, (doi: 10.1016/j.ememar.2024.101209)

Otto, P., Fasso, A., Maranzano, P. (2024) A review of regularised estimation methods and cross-validation in spatiotemporal statistics. Statistics Surveys,

Ge, Y., Husmeier, D., Rabbani, A., Gao, H. (2024) Advanced statistical inference of myocardial stiffness: A time series Gaussian Process approach of emulating cardiac mechanics for real-time clinical decision support. Computers in Biology and Medicine,

Dorau, K., Rückamp, D., Weber, C., Scheeder, G., Reßing, R., Peth, S., Otto, P., Altmann, K., Fries, E., Hoppe, M. (2024) Characterization and spatial distribution of mesoplastics in an arable soil. European Journal of Soil Science,

Belmont, J., Martino, S., Illian, J., Rue, H. (2024) Spatio-temporal occupancy models with INLA. Methods in Ecology and Evolution, 15, pp. 2087-2100. (doi: 10.1111/2041-210X.14422)

Hawkes, C., Morris, J., Bavcar, S., Wilkie, C., Ray, S., Auquier, C., Benjamin, S., Massó, J. B., Bottin, S., Davies, O., Desmas‐Bazelle, I., Einhorn, A., Figueroa‐Gonzalez, C., Holenova, K., Kritsotalaki, E., Peak, K., Smallwood, K., Treggiari, E., Valenti, P., de la Virgen, M. G., Fournier, Q. (2024) Comparison of CHOP ‐19 and CHOP ‐25 for treatment of peripheral nodal B‐cell lymphoma in dogs: a European multicenter retrospective cohort study. Journal of Veterinary Internal Medicine, (doi: 10.1111/jvim.17222)

Zhu, Q., Lee, D., Stoner, O. (2024) A comparison of statistical and machine learning models for spatio-temporal prediction of ambient air pollutant concentrations in Scotland. Environmental and Ecological Statistics,

Byrne, M. et al. (2024) Theory and the future of land-climate science. Nature Geoscience, (doi: 10.1038/s41561-024-01553-8)

Paun, L. M., Colebank, M. J., Husmeier, D. (2024) A comparison of Gaussian processes and polynomial chaos emulators in the context of haemodynamic pulse-wave propagation modelling. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences,

(2024) Advanced Statistical Methods in Process Monitoring, Finance, and Environmental Science: Essays in Honour of Wolfgang Schmid.

Paun, L. M., Colebank, M. J., Taylor-LaPole, A., Olufsen, M. S., Ryan, W., Murray, I., Salter, J. M., Applebaum, V., Dunne, M., Hollins, J., Kimpton, L., Volodina, V., Xiong, X., Husmeier, D. (2024) SECRET: Statistical Emulation for Computational Reverse Engineering and Translation with applications in healthcare. Computer Methods in Applied Mechanics and Engineering, 430, (doi: 10.1016/j.cma.2024.117193)

Mamalakis, M., Banerjee, A., Ray, S., Wilkie, C., Clayton, R. H., Swift, A. J., Panoutsos, G., Vorselaars, B. (2024) Deep multi-metric training: the need of multi-metric curve evaluation to avoid weak learning. Neural Computing and Applications, 36, pp. 18841-18862. (doi: 10.1007/s00521-024-10182-6)

Teale, C., Colombo, P., Villejo, S. J., McInnes, C. (2024) Gaussian process for modelling the space environment from sparse data collected by massively distributed femtosatellite networks.

Mattera, R., Otto, P. (2024) Network log-ARCH models for forecasting stock market volatility. International Journal of Forecasting, 40, pp. 1539-1555. (doi: 10.1016/j.ijforecast.2024.01.002)

Stoner, O., Economou, T., Brown, A.R. (2024) Seasonal early warning of impacts of harmful algal blooms on farmed shellfish in coastal waters of Scotland. Water Resources Research, 60, (doi: 10.1029/2023WR034889)

MacBride, C., Davies, V., Lee, D. (2024) A spatial autoregressive random forest algorithm for small-area spatial prediction. Annals of Applied Statistics,

Bryce, E., Castro-Camilo, D., Dashwood, C., Tanyas, H., Ciurean, R., Novellino, A., Lombardo, L. (2024) An updated landslide susceptibility model and a log-Gaussian Cox process extension for Scotland. Landslides, (doi: 10.1007/s10346-024-02368-9)

LI, M., Cuba, D., Hu, C., Castro-Camilo, D. (2024) A wee exploration of techniques for risk assessments of extreme events. Extremes,

Hawkes, C., Morris, J., Bavcar, S., Wilkie, C., Ray, S., Auqier, C., Benjamin, S., Borrego Massó, J., Bottin, S., Davies, O., Desmas-Bazelle, I., Einhorn, A., Figueroa-Gonzalez, C., Holenova, K., Kritsotalaki:, E., Smallwood, K., Treggiari, E., Valenti, P., de la Virgen, M. G., Fournier, Q. (2024) Comparison of CHOP-19 and CHOP-25 for treatment of canine peripheral nodal B-cell lymphomas: a European multicenter retrospective cohort study. Journal of Veterinary Internal Medicine,

Zhang, W., Banerjee, A., Ray, S. (2024) Self-supervised instance segmentation of diabetic foot ulcers via feature correspondence distillation.

Scott, M. (2024) Uncertainty and error in radiocarbon dating. Elsevier

Dalton, D., Lazarus, A., Gao, H., Husmeier, D. (2024) Boundary constrained Gaussian processes for robust physics-informed machine learning of linear partial differential equations. Journal of Machine Learning Research,

Brass, D. P., Cobbold, C. A., Purse, B. V., Ewing, D. A., Callaghan, A., White, S. M. (2024) Role of vector phenotypic plasticity in disease transmission as illustrated by the spread of dengue virus by Aedes albopictus. Nature Communications, 15, (doi: 10.1038/s41467-024-52144-5)

Schneider, M. J., Browell, J., Rankin, R. (2024) Limiting extreme behavior in forecasting competitions. Foresight, 75, pp. 32-37.

Khoo, T. H., Pathmanathan, D., Otto, P., Dabo‐Niang, S. (2024) A Markov‐switching spatio‐temporal ARCH model. Stat, 13, (doi: 10.1002/sta4.713)

Meis, M., Pirani, M., Euan, C., Castruccio, S., Simmons, S., Stroud, J.R., Blangiardo, M., Wikle, C.K., Wheeler, M., Naumova, E., Bravo, L., Miller, C., Gel, Y. (2024) Catalysing virtual collaboration: The experience of the remote TIES working groups. Environmetrics, 35, (doi: 10.1002/env.2855)

Low, M. I., Bowman, A. W., Jones, W., Bonte, M. (2024) Exact optimisation of spatiotemporal monitoring networks by p-splines with applications in groundwater assessment. Environmetrics, 35, (doi: 10.1002/env.2874)

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, 153, (doi: 10.1016/j.patcog.2024.110485)

Li, R., Mair, C. (2024) Exploring Perspectives on Decolonising STEM Education: Insights from Mathematics and Statistics.

Reid, J., Gildea, E., Davies, V., Thompson, J., Scott, M. (2024) Measuring the effect of the anti-nerve growth factor antibodies bedinvetmab and frunevetmab on quality of life in dogs and cats with osteoarthritis using a validated health-related quality of life outcome measure: an observational real-world study. Frontiers in Veterinary Science, 11, (doi: 10.3389/fvets.2024.1395360)

Paun, M., Fensterseifer Schmidt, A., Mcginty, S., Husmeier, D. (2024) Constrained Bayesian optimization with a cardiovascular application. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 480, (doi: 10.1098/rspa.2023.0371)

Colebank, M. J., Oomen, P. A., Witzenburg, C. M., Grosberg, A., Beard, D. A., Husmeier, D., Olufsen, M. S., Chesler, N. C. (2024) Guidelines for mechanistic modeling and analysis in cardiovascular research. American Journal of Physiology - Heart and Circulatory Physiology, 327, pp. H473-H503. (doi: 10.1152/ajpheart.00766.2023)

Enright, J., Lee, D., Meeks, K., Sylvester, J., Pettersson, W. (2024) The complexity of finding and enumerating optimal subgraphs to represent spatial correlation. Algorithmica, (doi: 10.1007/s00453-024-01256-x)

Bartolo, M. A., Taylor-LaPole, A. M., Gandhi, D., Johnson, A., Li, Y., Slack, E., Stevens, I., Turner, Z., Puelz, C., Husmeier, D., Olufsen, M. S. (2024) Computational framework for the generation of one-dimensional vascular models accounting for uncertainty in networks extracted from medical images. Journal of Physiology, 602, pp. 3929-3954. (doi: 10.1113/JP286193)

Ahovègbé, L., Shah, R., Kpossou, A. R., Davis, C., Niebel, M., Filipe, A., Goldstein, E., Alassan, K. S., Keke, R., Sehonou, J., Kodjoh, N., Gbedo, S. E., Ray, S., Wilkie, C., Vattipally, S., Tong, L., Kamba, P. F., Gbenoudon, S. J., Gunson, R., Ogwang, P., Thomson, E. C. (2024) Hepatitis C virus diversity and treatment outcomes in Benin; a prospective cohort study. Lancet Microbe, 5, pp. 697-706. (doi: 10.1016/S2666-5247(24)00041-7)

Jack, E., Alexander, C., Jones, E. M. (2024) Exploring the impact of gamification on engagement in a statistics classroom. Teaching Mathematics and its Applications: An International Journal of the IMA, (doi: 10.1093/teamat/hrae009)

Gazioglu, S., Scott, E. M. (2024) Determining input factor importance in a compartmental model using screening methods. Communications in Statistics: Simulation and Computation, (doi: 10.1080/03610918.2024.2357180)

Otto, P., Doğan, O., Taspinar, S., Schmid, W., Bera, A. (2024) Spatial and spatiotemporal volatility models: a review. Journal of Economic Surveys, (doi: 10.1111/joes.12643)

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, 71, pp. 757-760. (doi: 10.1090/noti2958)

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), 73, pp. 715-734. (doi: 10.1093/jrsssc/qlae008)

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., Brown, K., Beekman, E., Olowe, E., Macaulay, C., Fu, J., Parodi, A. (2024) Embedding Communication into the Statistics Currciulum.

Zou, Z., O'Donnell, R., Miller, C., Lee, D., Wilkie, C. (2024) A computationally efficient spatio-temporal fusion model for reflectance data.

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), (doi: 10.1093/jrsssa/qnae036)

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,

Dalton, D., Husmeier, D., Gao, H. (2024) Physics and Lie Symmetry Informed Gaussian Processes.

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

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, 2024, (doi: 10.1111/ecog.07085)

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

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)

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)

Otto, P., Doğan, O., Taşpınar, S. (2024) Dynamic spatiotemporal ARCH models. Spatial Economic Analysis, 19, pp. 250-271. (doi: 10.1080/17421772.2023.2254817)

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)

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)

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)

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)

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.

Bee, S., Poole, J., Worden, K., Dervilis, N., Bull, L. (2024) Multitask feature selection within structural datasets. Data-Centric Engineering, 5, (doi: 10.1017/dce.2024.1)

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)

Nguyen, H. D., Gupta, M. (2023) Finite sample inference for empirical Bayesian methods. Scandinavian Journal of Statistics, 50, pp. 1616-1640. (doi: 10.1111/sjos.12643)

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.

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)

Stoner, O., Halliday, A., Economou, T. (2023) Correcting delayed reporting of COVID-19 using the generalized-Dirichlet-multinomial method. Biometrics, 79, pp. 2537-2550. (doi: 10.1111/biom.13810)

Lee, D., Anderson, C. (2023) Delivering spatially comparable inference on the risks of multiple severities of respiratory disease from spatially misaligned disease count data. Biometrics, 79, pp. 2691-2704. (doi: 10.1111/biom.13739)

Zhang, W., Bonner, S. J., McCrea, R. (2023) Latent multinomial models for extended batch-mark data. Biometrics, 79, pp. 2732-2742. (doi: 10.1111/biom.13789)

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)

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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)

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

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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)

Bull, L.A., Worden, K., Dervilis, N. (2020) Towards semi-supervised and probabilistic classification in structural health monitoring. Mechanical Systems and Signal Processing, 140, (doi: 10.1016/j.ymssp.2020.106653)

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)

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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)

Bull, L.A., Rogers, T.J., Wickramarachchi, C., Cross, E.J., Worden, K., Dervilis, N. (2019) Probabilistic active learning: An online framework for structural health monitoring. Mechanical Systems and Signal Processing, 134, (doi: 10.1016/j.ymssp.2019.106294)

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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)

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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)

Bull, L.A., Worden, K., Fuentes, R., Manson, G., Cross, E.J., Dervilis, N. (2019) Outlier ensembles: A robust method for damage detection and unsupervised feature extraction from high-dimensional data. Journal of Sound and Vibration, 453, pp. 126-150. (doi: 10.1016/j.jsv.2019.03.025)

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)

Dervilis, N., Zhang, T., Bull, L.A., Cross, E.J., Rogers, T.J., Fuentes, R., Dertimanis, V., Abdallah, I., Chatzi, E., Worden, K. (2019) A Nonlinear Robust Outlier Detection Approach for SHM.

Bull, L.A., Worden, K., Rogers, T., Wickramarachchi, C., Cross, E., McLeay, T., Leahy, W., Dervilis, N. (2019) A probabilistic framework for online structural health monitoring : active learning from machining data streams. Journal of Physics : Conference Series, 1264, (doi: 10.1088/1742-6596/1264/1/012028)

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)

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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)

Bull, L.A., Worden, K., Rogers, T., Cross, E., Dervilis, N. (2019) Investigating engineering data by probabilistic measures. Springer International Publishing

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.

Bull, L.A., Worden, K., Dervilis, N. (2019) Damage classification using labelled and unlabelled measurements. DEStech Publications, Inc.

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)

Bull, L.A., Dervilis, N., Worden, K. (2019) Experimental validation of the population-form to represent nominally-identical systems. DEStech Publications, Inc.

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.

Bull, L., Worden, K., Manson, G., Dervilis, N. (2018) Active learning for semi-supervised structural health monitoring. Journal of Sound and Vibration, 437, pp. 373-388. (doi: 10.1016/j.jsv.2018.08.040)

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)

Bull, L.A., Manson, G., Worden, K., Dervilis, N. (2018) Active learning approaches to structural health monitoring. Springer International Publishing

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)

Pamphilis, N., Barg, K., Fogarty, B., Dean, N. (2018) Interactive tools for visualizing quantitative data to promote statistical literacy among Social Science students: an example of the use of R-shiny.

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)

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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