Jenny Wadsworth

I'm currently a professor of statistics in the School of Mathematical Sciences, Lancaster University. You can contact me at: j (dot) wadsworth (at) lancaster (dot) ac (dot) uk

I'm intersted in all aspects of extreme value theory. I'm particularly keen on developing new statistical methodology for extremes, motivated by deficiencies of existing frameworks for practical problems. I'm also interested in copulas, spatial statistics, environmental statistics, likelihood-based inference, and nonparametric inference.

• Andre, L. M., Wadsworth, J. L. and Huser, R. G. (2025) Neural Bayes estimation and selection of complex bivariate extremal dependence models. (arxiv) Extremes, to appear.
• Huser, R., Opitz, T., and Wadsworth, J. L. (2025) Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes.(arxiv) Environmental Data Science, 4 e3.
• Kakampakou, L., Simpson, E. S. and Wadsworth, J. L. (2024) Spatial extremal modelling: A case study on the interplay between margins and dependence.(arxiv) Stat, 13 (4), e70021.
• Murphy-Barltrop, C. J. R. and Wadsworth, J. L. (2024) Modelling non-stationarity in asymptotically independent extremes.(arxiv) CSDA, 199 108025.
• Murphy-Barltrop, C. J. R., Wadsworth, J. L. and Eastoe, E.F. (2024) Improving estimation for asymptotically independent bivariate extremes via global estimators for the angular dependence function. (arxiv) Extremes, 27 (4), 643-671.
• Wadsworth, J. L. and Campbell, R. (2024) Statistical inference for multivariate extremes via a geometric approach.(arxiv) JRSSB, 86 (5), 1243-1265. Associated R package here.
• Andre, L. M., Wadsworth, J. L. and O'Hagan, A. (2024) Joint modelling of the body and tail of bivariate data. CSDA. 189 107841.
• Simpson, E. S., Opitz, T. and Wadsworth, J. L. (2023). High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields. Extremes. 26 669-713
• Murphy-Barltrop, C. J. R., Wadsworth, J. L. and Eastoe, E. F. (2023) New estimation methods for extremal bivariate return curves. Environmetrics. 34 (5) e2797
• Wadsworth, J. L. and Tawn, J. A. (2022). Higher-dimensional spatial extremes via single-site conditioning. (arxiv) Spatial Statistics 51:100667. Earlier version with title "Spatial conditional extremes". (here)
• Zhang, Z., Huser, R., Opitz, T. and Wadsworth, J. L. (2022) Modeling spatial extremes using normal mean-variance mixtures. Extremes, 25, 175-197
• Huser, R. G. and Wadsworth, J. L. (2022) Advances in statistical modeling of spatial extremes. Wiley Interdisciplinary Reviews: Computational Statistics, 14 (1), e1537.
• Zhang, L., Shaby, B. A. and Wadsworth, J. L. (2022) Hierarchical transformed scale mixtures for flexible modeling of spatial extremes on datasets with many locations. (arxiv) JASA, 117, 1357-1369.
• Nolde, N. and Wadsworth, J. L. (2022). Linking representations for multivariate extremes via a limit set. Advances in Applied Probability. (Earlier title: "Connections between representations for multivariate extremes"; arxiv)
• Simpson, E. S., Wadsworth, J. L. and Tawn, J. A. (2021) A geometric investigation into the tail dependence of vine copulas. Journal of Multivariate Analysis, 184.
• Richards, J. and Wadsworth, J. L. (2021) Spatial deformation for nonstationary extremal dependence. Environmetrics, 32(5) e2671.
• Simpson, E. S. and Wadsworth, J. L. (2021) Conditional modelling of spatio-temporal extremes for Red Sea surface temperatures. (arxiv) Spatial Statistics. 41
• Jonathan, P., Randell, D., Wadsworth, J. L. and Tawn, J. A. (2021) Uncertainties in return values from extreme value analysis of peaks over threshold using the generalised Pareto distribution. Ocean Engineering, 220.
• Simpson, E. S., Wadsworth, J. L. and Tawn, J. A. (2020). Determining the dependence structure of multivariate extremes. Biometrika, 107(3), 513-532.
• Engelke, S., Opitz, T. and Wadsworth, J. L. (2019). Extremal dependence of random scale constructions. Extremes, 22(4), 623-666.
• Kiriliouk, A., Rootzén, H., Segers, J. and Wadsworth, J. L. (2019). Peaks over thresholds modelling with multivariate generalized Pareto distributions. Technometrics, 61(1), 123-135.
• Huser, R. G. and Wadsworth J. L. (2019). Modeling spatial processes with unknown extremal dependence class. JASA 114, 434-444
• Rootzén, H., Segers, J. and Wadsworth, J. L. (2018). Multivariate generalized Pareto distributions: parametrizations, representations, and properties. Journal of Multivariate Analysis, 165 117-131
• Castro, D. A., de Carvalho, M. and Wadsworth, J. L. (2018). Time-varying extreme value dependence with application to leading European stock markets. Annals of Applied Statistics., 12(1) 283-309.
• Rootzén, H., Segers, J. and Wadsworth, J. L. (2018). Multivariate peaks over thresholds models. Extremes, 21(1) 115-145.
• Wadsworth, J. L., Tawn, J. A., Davison, A. C. and Elton, D. M. (2017). Modelling across extremal dependence classes. JRSSB, 79(1), 149-175. Preprint version including Supplementary Material (N.B. See below for associated R package)
• Wadsworth, J. L. (2016). Exploiting structure of maximum likelihood estimators for extreme value threshold selection. Technometrics, 58(1), 116-126. Preprint version including Supplementary Material R code
• Wadsworth, J. L. (2015). On the occurrence times of componentwise maxima and bias in likelihood inference for multivariate max-stable distributions. Biometrika 102(3) 705-711.
• Wadsworth, J. L. and Tawn, J. A. (2014). Efficient inference for spatial extreme value processes associated to log-Gaussian random functions. Biometrika, 101(1), 1-15
• Wadsworth, J. L. and Tawn, J. A. (2013). A new representation for multivariate tail probabilities. Bernoulli, 19(5B), 2689-2714 (pdf)
• Wadsworth, J. L. and Tawn, J. A. (2012). Dependence modelling for spatial extremes. Biometrika, 99, 253-272
• Wadsworth, J. L. and Tawn, J. A. (2012). Likelihood-based procedures for threshold diagnostics and uncertainty in extreme value modelling. JRSSB, 74, 543-567
• Wadsworth, J. L., Tawn, J. A., and Jonathan, P. (2010). Accounting for choice of measurement scale in extreme value modelling. Annals of Applied Statistics, 4, 1558-1578 (pdf)

Discussion contributions

• Wadsworth, J. L. (2020) Discussion of "Graphical models for extremes", by Engelke and Hitz (pdf). JRSSB, 82(4), 908-910.
• Gabda, D., Towe, R., Wadsworth, J. L. and Tawn, J. A. (2012). Discussion of "Statistical modelling of spatial extremes". Statistical Science, 27, 189-192 (pdf)
• Papastathopoulos, I., Tawn, J. A. and Wadsworth, J. L. (2011). Discussion of: Threshold modelling of spatially-dependent non-stationary extremes with applications to hurricane-induced wave heights. Environmetrics, 22, 813-814

Submitted / under revision

• Papastathopulos, I. and Wadsworth, J. L. (2026) Geometric extremal graphical models and coefficients of extremal dependence on block graphs. (arxiv)
• Lee, J. and Wadsworth, J. L. (2025) Geometric criteria for identifying extremal dependence and flexible modeling via additive mixtures. (arxiv)
• Kakampakou, L. and Wadsworth, J. L. (2025) Geometric modelling of spatial extremes. (arxiv)
• Murphy-Barltrop, C. J. R., Wadsworth, J. L., de Carvalho, M., Youngman B. D. (2025) Modelling non-stationary extremal dependence through a geometric approach. (arxiv)
• Campbell, R. and Wadsworth, J. L. (2024) Piecewise-linear modeling of multivariate geometric extremes. (arxiv)

R packages


• EVcopula (or as a .zip) to accompany the paper Modelling across extremal dependence classes. Example code to help installation / familiarisation with features.
• SpatialADAI to accompany the paper Modeling spatial processes with unknown extremal dependence class. Example code.
• geometricMVEorig to accompany the paper Statistical inference for multivariate extremes via a geometric approach. Example code. NB: this package is retained and renamed from geometricMVE to geometricMVEorig for back-compatability: see below for newer package with more functionality
• geometricMVE Available at github: includes code for both parametric and piecewise linear modelling. Follow instructions at the github page to install.


• Packages may be updated at any time. Please inform me of any bugs at the address at the top of the page... thanks!

Other

R code for Exploiting structure of maximum likelihood estimators for extreme value threshold selection. See also Léo Belzile's R package mev.

Lancaster

• Stor605 Inference and Modelling (for MRes Statistics and Operational Research)
• Math562 Extreme Value Theory (for MSc Statistics / Data Science)
• Math482/582 Assessing financial risk: extreme value methods (for MSci Mathematics / MSc Quantitative Finance)

Cambridge

• Applied Statistics (for Part III) -- mostly GLMs

EPFL

• Applied Statistics (for Masters program) -- various topics in applied statistics