• *: Corresponding author
  • Underlined: PhD student or Postdoc advisees under my (co-)supervision at the start of main work

Under review or revision:

  • [24] Fischer*, E., Bador, M., Huser, R., Kendon, E. J., Robinson, A., and Sippel, S. (2024+), Record-breaking extremes in a warming climate, arXiv preprint 24XX.XXXXX [arXiv][PDF]

  • [23] Zammit-Mangion*, A., Sainsbury-Dale, M., and Huser, R. (2024+), Neural methods for amortised parameter inference, arXiv preprint 2404.12484 [arXiv][PDF]

  • [22] Redondo, P. V., Guerrero, M., Ombao*, H., and Huser, R. (2024+), Statistics of extremes for neuroscience, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL [arXiv][PDF]

  • [21] Yadav, R., Lombardo*, L. and Huser, R. (2024+), Statistics of extremes for natural hazards: landslides and earthquakes, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL [arXiv][PDF]

  • [20] Huser*, R., Opitz, T., and Wadsworth, J. L. (2024+), Modeling of spatial extremes in environmental data science: Time to move away from max-stable processes, arXiv preprint 2401.17430 [arXiv][PDF]

  • [19] Dahal*, A., Huser, R., and Lombardo, L. (2024+), At the junction between deep learning and statistics of extremes: formalizing the landslide hazard definition, arXiv preprint 2401.14210 [arXiv][PDF]

  • [18] Richards, J. and Huser*, R. (2023+), Extreme quantile regression with deep learning, In the Handbook of Statistics of Extremes, Editors M. de Carvalho, R. Huser, P. Naveau, and B. Reich, Chapman & Hall/CRC Press, Boca Raton, FL [arXiv][PDF]

  • [17] Krupskii*, P., Huser, R. (2023+), Max-convolution processes with random shape indicator kernels, arXiv preprint 2310.10588 [arXiv][PDF]

  • [16] Kim, M., Genton, M. G., Huser, R., and Castruccio*, S. (2023+), A neural network-based approach to normality testing for dependent data, arXiv preprint 2310.10422 [arXiv][PDF]

  • [15] Sainsbury-Dale*, M., Richards, J., Zammit-Mangion, A., and Huser, R. (2023+), Neural Bayes estimators for irregular spatial data using graph neural networks, arXiv preprint 2310.02600 [arXiv][PDF]

    • ⇒SBSS Student Paper Award 2024, Section on Bayesian Statistical Science (SBSS), ASA

  • [14] Zhang*, Z., Bolin, D., Engelke, S., and Huser, R. (2023+), Extremal dependence of moving average processes driven by exponential-tailed Lévy noise, arXiv preprint 2307.15796 [arXiv][PDF]

  • [13] Vandeskog*, S. M., Martino, S., and Huser, R. (2023+), Fast spatial simulation of extreme high-resolution radar precipitation data using INLA, arXiv preprint 2307.11390 [arXiv][PDF]

  • [12] Zhang*, L., Ma, X., Wikle, C. K., and Huser, R. (2023+), Flexible and efficient spatial extremes emulation via variational autoencoders, arXiv preprint 2307.08079 [arXiv][PDF]

  • [11] Richards*, J., Sainsbury-Dale, M., Zammit-Mangion, A., and Huser, R. (2023+), Neural Bayes estimators for censored inference with peaks-over-threshold models, arXiv preprint 2306.15642 [arXiv][PDF]

  • [10] Redondo*, P. V., Huser, R., and Ombao, H. (2023+), Measuring information transfer between nodes in a brain network through spectral transfer entropy, arXiv preprint 2303.06384 [arXiv][PDF]

    • ⇒Best Poster Presentation Award, 2023 Extreme-Value Analysis conference (EVA 2023), Milan, IT

    • ⇒Runner-up of Best Student Paper Award 2023, Section on Statistics in Imaging (SI), ASA

  • [9] Oesting*, M., and Huser, R. (2022+), Patterns in spatio-temporal extremes, arXiv preprint 2212.11001 [arXiv][PDF]

  • [8] Guerrero, M. B., Ombao, H., and Huser*, R. (2022+), Club Exco: clustering brain extreme communities from multi-channel EEG data, arXiv preprint 2212.04338 [arXiv][PDF]

  • [7] Zhong, P., Brunner, M., Opitz, T., and Huser*, R. (2022+), Spatial modeling and future projection of extreme precipitation extents, arXiv preprint 2212.03028 [arXiv][PDF]

  • [6] Shao, X., Hazra, A., Richards, J., and Huser*, R. (2022+), Flexible modeling of nonstationary extremal dependence using spatially-fused LASSO and ridge penalties, arXiv preprint 2210.05792 [arXiv][PDF]

  • [5] Vandeskog*, S. M., Martino, S., and Huser, R. (2022+), An efficient workflow for modelling high-dimensional spatial extremes, arXiv preprint 2210.00760 [arXiv][PDF]

  • [4] Redondo*, P. V., Huser, R., and Ombao, H. (2022+), Functional-coefficient models for multivariate time series in designed experiments: with applications to brain signals, arXiv preprint 2208.00292 [arXiv][PDF] 

  • [3] Richards*, J., and Huser, R. (2022+), Regression modelling of spatiotemporal extreme U.S. wildfires via partially-interpretable neural networks, arXiv preprint 2208.07581 [arXiv][PDF]

  • [2] Hazra, A., Huser*, R., and Bolin, D. (2023+), Efficient modeling of spatial extremes over large geographical domains, arXiv preprint 2112.10248 [arXiv][PDF]

  • [1] Hazra*, A., Alahmadi, E., and Huser, R. (2021+), Extreme-value analysis: a brief summary, submitted [arXiv][PDF]

Journal papers: 

  • [62] Gong, Y., Zhong, P., Opitz, T., and Huser*, R. (2024+), Partial tail-correlation coefficient applied to extremal-network learning, Technometrics, to appear [journal][PDF preprint]

  • [61] Cisneros, D., Hazra, A., and Huser*, R. (2023+), Spatial wildfire risk modeling using a tree-based multivariate generalized Pareto mixture model, Journal of Agricultural, Biological, and Environmental Statistics, to appear [journal][PDF preprint]

  • [60] Huser*, R., Stein, M. L., and Zhong, P. (2023+), Vecchia likelihood approximation for accurate and fast inference with intractable spatial max-stable models, Journal of Computational and Graphical Statistics, to appear [journal][PDF preprint]

  • [59] Zhong, P., Huser*, R., and Opitz, T. (2024), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics 30, 96-109 [journal][PDF preprint]

  • [58] Dahal, A., Tanyas, H., van Westen, C., van der Meije, M., Mai, P. M, Huser, R., and Lombardo*, L. (2024), Space-time landslide hazard modeling via ensemble neural networks, Natural Hazards and Earth System Sciences 24, 823-845 [journal][PDF preprint]

  • [57] Sainsbury-Dale*, M., Zammit-Mangion, A., and Huser, R. (2024), Likelihood-free parameter estimation with neural Bayes estimators, The American Statistician 78, 1-14 [journal][PDF preprint] 

  • [56] Cisneros, D., Richards*, J., Dahal, A., Lombardo, L., and Huser, R. (2024), Deep graphical regression models for jointly moderate and extreme Australian wildfires, Spatial Statistics 59, 100811 [journal][PDF preprint]

  • [55] Yadav, R., Huser*, R., Opitz, T., and Lombardo, L. (2023), Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions, Journal of the Royal Statistical Society: Series C 72, 1139-1161 [journal][PDF preprint]

  • [54] Ahmed*, M., Tanyas, H., Huser, R., Dahal, A., Titti, G., Borgatti, L., Francioni, L., and Lombardo L. (2023), Dynamic rainfall-induced landslide susceptibility: a step towards a unified forecasting system, International Journal of Applied Earth Observation and Geoinformation 125, 103593 [journal][PDF preprint]

  • [53] Richards*, J., Huser, R., Bevacqua, E., and Zscheischler, J. (2023), Insights into the drivers and spatio-temporal trends of extreme Mediterranean wildfires with statistical deep-learning, Artificial Intelligence for the Earth Systems 2, e220095 [journal][PDF preprint]

  • [52] Dahal*, A., Castro Cruz, D. A., Tanyas, H., Fadel, I., Mai, P. M., van der Meijde, M., van Westen, C., Huser, R., and Lombardo, L. (2023), From ground motion simulations to landslide occurrence prediction, Geomorphology 441, 108898 [journal][PDF preprint]

  • [51] Zhang, Z., Arellano-Valle, R. B., Genton, M. G., and Huser*, R. (2023), Tractable Bayes of skew-elliptical link models for correlated binary data, Biometrics 79, 1788-1800 [journal][PDF preprint]

  • [50] Zhang*, Z., Krainski, E., Zhong, P., Rue, H., and Huser, R. (2023), Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, Extremes 26, 339-351 [journal][PDF preprint]

  • [49] Cisneros, D., Gong, Y., Yadav, R., Hazra*, A., and Huser, R. (2023), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, Extremes 26, 301-330 [journal][PDF preprint]

  • [48] de Carvalho*, M., Huser, R., and Rubio, R. (2023), Similarity-based clustering for patterns of extreme values, Stat 12, e560 [journal][PDF preprint]

  • [47] Guerrero, M. B., Huser*, R., and Ombao, H. (2023), Conex-Connect: Learning patterns in extremal brain connectivity from multi-channel EEG data, Annals of Applied Statistics 17, 178-198 [journal][PDF preprint]

  • [46] Gong, Y., and Huser*, R. (2022), Flexible modeling of multivariate spatial extremes, Spatial Statistics 52, 100713 [journal][PDF preprint]

  • [45] Krupskii, P., and Huser*, R. (2022), Modeling spatial tail dependence with Cauchy convolution processes, Electronic Journal of Statistics 16, 6135-6174 [journal][PDF preprint]

  • [44] Castro-Camilo*, D., Huser, R., and Rue, H. (2022), Practical strategies for generalized extreme value-based regression models for extremes, Environmetrics 33, e2742 [journal][PDF preprint]

  • [43] Zhang, Z., Huser*, R., Opitz, T., and Wadsworth, J. L. (2022), Modeling spatial extremes using normal mean-variance mixtures, Extremes 25, 175-197 [journal][PDF preprint]

  • [42] Gong, Y., and Huser*, R. (2022), Asymmetric tail dependence modeling, with application to cryptocurrency market data, Annals of Applied Statistics 16, 1822-1847 [journal][PDF preprint]

  • [41] Opitz*, T., Bakka, H., Huser, R., and Lombardo, L. (2022), High-resolution Bayesian mapping of landslide hazard with unobserved trigger event, Annals of Applied Statistics 16, 1653-1675 [journal][PDF preprint]

  • [40] Jóhannesson, Á. V., Siegert, S., Huser*, R., Bakka, H., and Hrafnkelsson, B. (2022), Approximate Bayesian inference for analysis of spatio-temporal flood frequency data, Annals of Applied Statistics 16, 905-935[journal][PDF preprint] 

  • [39] Yadav, R., Huser*, R., and Opitz, T. (2022), A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data, Spatial Statistics 51, 100672 [journal][PDF preprint]

  • [38] Zhong, P., Huser*, R., and Opitz, T. (2022), Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude, Annals of Applied Statistics 16, 272-299 [journal][PDF preprint]

  • [37] Huser*, R., and Wadsworth, J. L. (2022), Advances in statistical modeling of spatial extremes, Wiley Interdisciplinary Reviews (WIREs): Computational Statistics 14, e1537 [journal][PDF preprint]

  • [36] Lombardo*, L., Tanyas, H., Huser, R., Guzzetti, F., and Castro-Camilo, D. (2021), Landslide size matters: a new data-driven, spatial prototype, Engineering Geology 293, 106288 [journal][PDF preprint]

  • [35] Hazra, A., and Huser*, R. (2021), Estimating high-resolution Red Sea surface temperature hotspots, using a low-rank semiparametric spatial model, Annals of Applied Statistics 15, 572-596[journal][PDF preprint]

  • [34] Hrafnkelsson*, B., Siegert, S., Huser, R., Bakka, H., and Jóhannesson, Á. V. (2021), Max-and-Smooth: a two-step approach for approximate Bayesian inference in latent Gaussian models, Bayesian Analysis 16, 611-638[journal][PDF preprint]

  • [33] Yadav, R., Huser*, R., and Opitz, T. (2021), Spatial hierarchical modeling of threshold exceedances using rate mixtures, Environmetrics 32, e2662[journal][PDF preprint]

  • [32] Bopp*, G., Shaby, B., and Huser, R. (2021), A hierarchical max-infinitely divisible spatial model for extreme precipitation, Journal of the American Statistical Association 116, 93-106 [journal][PDF preprint]

  • [31] Huser*, R. (2021), Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes, Extremes 24, 91-104[journal][PDF preprint]

  • [30] Huser*, R., Opitz, T., and Thibaud, E. (2021), Max-infinitely divisible models and inference for spatial extremes, Scandinavian Journal of Statistics 48, 321-348 [journal][PDF preprint]

  • [29] Khandavilli*, M., Yalamanchi, K. K., Huser, R., and Sarathy, M. (2020), Effects of fuel composition variability on high temperature combustion properties: A statistical analysis, Applications in Energy and Combustion Science 1-4, 100012[journal][PDF preprint]

  • [28] Lombardo*, L., Opitz, T., Ardizzone, F., Guzzetti, F., and Huser, R. (2020), Space-time landslide predictive modelling, Earth-Science Reviews 209, 103318[journal][PDF preprint]

  • [27] Castro Camilo*, D., and Huser, R. (2020), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, Journal of the American Statistical Association 115, 1037-1054 [journal][PDF preprint]

  • [26] Vettori, S., Huser*, R., Segers, J., and Genton, M. G. (2020), Bayesian model averaging over tree-based dependence structures for multivariate extremes, Journal of Computational and Graphical Statistics 29, 174-190 [journal][PDF preprint]

    • ⇒ENVR Student Paper Award 2017, Section on Statistics and the Environment (ENVR), ASA

  • [25] Alam, T., Alazmi, M., Naser, R., Huser, F., Momin, A. A., Astro, V., Hong, S., Walkiewicz, K. W., Canlas, C. G., Huser, R., Ali, A., Merzaban, J., Adamo, A., Jaremko, M., Jaremko, Ł., Bajic, V. B., Gao, X., and Arold, S. T. (2020), Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs, Bioinformatics 36, 1121-1128 [journal][PDF preprint]

  • [24] Vettori, S., Huser*, R., and Genton, M. G. (2019), Bayesian modeling of air pollution extremes using nested multivariate max-stable processes, Biometrics 75, 831-841 [journal][PDF preprint]

    • ⇒Distinguished Student Paper Award 2018, Eastern North American Region (ENAR) of the International Biometric Society

  • [23] Castro-Camilo*, D., Huser, R., and 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, 517-534 [journal][PDF preprint]

  • [22] Lombardo*, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2019), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, Journal of Geophysical Research: Earth Surface 124, 1958-1980 [journal][PDF preprint]

  • [21] Huser, R. and Wadsworth*, J. (2019), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association 114, 434-444 [journal][PDF preprint]

  • [20] Huser*, R., Dombry, C., Ribatet, M., and Genton, M. G. (2019), Full likelihood inference for max-stable data, Stat 8, e218 [journal][PDF preprint]

  • [19] Opitz, T., Huser*, R., Bakka, H., and Rue, H. (2018), INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles, Extremes 21, 441-462 [journal][PDF preprint]

  • [18] Lombardo*, L., Opitz, T., and Huser, R. (2018), Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster, Stochastic Environmental Research and Risk Assessment 32, 2179-2198 [journal][PDF preprint]

    • ⇒Highlighted among the 10 most downloaded 2018 papers in Springer's Environmental Sciences Journals (click here)

  • [17] Hofert*, M., Huser, R., and Prasad, A. (2018), Hierarchical archimax copulas, Journal of Multivariate Analysis 167, 195-211 [journal][PDF preprint]

  • [16] Krupskii*, P., Huser, R., and Genton, M. G. (2018), Factor copula models for replicated spatial data, Journal of the American Statistical Association 113, 467-479 [journal][PDF preprint]

  • [15] Vettori*, S., Huser, R., and Genton, M. G. (2018), A comparison of dependence function estimators in multivariate extremes, Statistics and Computing 28, 525-538 [journal][PDF preprint]

  • [14] Lombardo*, L., Saia, S., Schillaci, C., Mai, P. M., and Huser, R. (2018), Modeling soil organic carbon with Quantile Regression: Dissecting predictors’ effects on carbon stocks, Geoderma 318, 148-159 [journal][PDF preprint]

  • [13] Huser*, R., Opitz, T., and Thibaud, E. (2017), Bridging asymptotic independence and dependence in spatial extremes using Gaussian scale mixtures, Spatial Statistics 21, 166-186 [journal][PDF preprint]

  • [12] Castro Camilo, D., Lombardo*, L., Mai, P. M., Jie, D., and 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, 145-156 [journal][PDF preprint]

  • [11] Castruccio*, S., Huser, R., and Genton, M. G. (2016), High-order composite likelihood inference for max-stable distributions and processes, Journal of Computational and Graphical Statistics 25, 1212-1229 [journal][PDF preprint]

  • [10] Naveau*, P., Huser, R., Ribereau, P., and Hannart, A. (2016), Modeling jointly low, moderate and heavy rainfall intensities without a threshold selection, Water Resources Research 52, 2753-2769 [journal][PDF preprint]

  • [9] Huser*, R., and Genton, M. G. (2016), Non-stationary dependence structures for spatial extremes, Journal of Agricultural, Biological and Environmental Statistics 21, 470-491 [journal][PDF preprint]

    • ⇒Award for Best Paper published in JABES during 2016

  • [8] Huser*, R., Davison, A. C., and Genton, M. G. (2016), Likelihood estimators for multivariate extremes, Extremes 19, 79-103 [journal][PDF preprint]

  • [7] Ben Taieb*, S., Huser, R., Hyndman, R. J., and Genton, M. G. (2016), Forecasting uncertainty in electricity smart meter data by boosting additive quantile regression, IEEE Transactions on Smart Grid 7, 2448-2455 [journal][PDF preprint]

  • [6] Davison*, A. C., and Huser, R. (2015), Statistics of extremes, Annual Review of Statistics and its Application 2, 203-235 [journal][PDF preprint]

  • [5] Genton*, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y., and Vettori, S. (2015), Visuanimation in statistics, Stat 4, 81-96 [journal][PDF preprint]

  • [4] Huser, R., and Davison*, A. C. (2014), Space-time modelling of extreme events, Journal of the Royal Statistical Society: Series B 76, 439-461 [journal][PDF preprint]

  • [3] Davison*, A. C., Huser, R. and Thibaud, E. (2013), Geostatistics of dependent and asymptotically independent extremes, Mathematical Geosciences 45, 511-529 [journal][PDF preprint]

  • [2] Huser, R., and Davison*, A. C. (2013), Composite likelihood estimation for the Brown-Resnick process, Biometrika 100, 511-518 [journal][PDF preprint]

  • [1] Anderes*, E., Huser, R., Nychka, D., and Coram, M. (2013) Nonstationary positive definite tapering on the plane, Journal of Computational and Graphical Statistics 22, 848-865 [journal][PDF preprint]

Contributions to papers with discussion:

  • [4] Hazra*, A., and Huser, R. (2021), Discussion of "Multilevel linear models, Gibbs samplers and multigrid decompositions" by Giacomo Zanella and Gareth Roberts, Bayesian Analysis 16, 1309-1391 [journal][PDF preprint]

  • [3] Huser*, R., and Cisneros, D. (2020), Discussion of "Graphical models for extremes" by Sebastian Engelke and Adrien S. Hitz, Journal of the Royal Statistical Society: Series B 82, 871-932 [journal][PDF preprint] 

  • [2] Huser*, R., de Carvalho, M., and Lombardo, L. (2019), Discussion on the meeting on `Data visualization', Journal of the Royal Statistical Society: Series A 182, 419-441 [journal][PDF preprint]

  • [1] Bakka, H., Castro Camilo, D., Franco-Villoria, M., Freni-Sterrantino, A., Huser, R., Opitz, T., and Rue*, H. (2018), Discussion of "Using stacking to average Bayesian predictive distributions" by Yao et. al, Bayesian Analysis 13, 917-1003 [journal][PDF preprint]

Book chapters:

  • [3] Hazra, A., Huser*, R., and Jóhannesson, Á. V. (2023), Bayesian latent Gaussian models for high-dimensional spatial extremes, In Statistical Modeling Using Bayesian Latent Gaussian Models – With Applications in Geophysics and Environmental Sciences, editor B. Hrafnkelsson, Springer [arXiv][PDF]

  • [2] Lombardo*, L., Opitz, T., and Huser, R. (2019), Numerical recipes for landslide spatial prediction by using R-INLA: A step-by-step tutorial, In Spatial Modeling in GIS and R for Earth and Environmental Sciences, editors H. R. Pourghasemi and C. Gokceoglu, Elsevier, 55-83 [book][PDF preprint]

  • [1] Davison*, A. C., Huser, R., and Thibaud, E. (2019), Spatial extremes, In Handbook of Environmental and Ecological Statistics, editors A. E. Gelfand, M. Fuentes, J. A. Hoeting and R. L. Smith, CRC Press, 711-744 [book][PDF preprint]

PhD Thesis:

  • [1] Huser*, R. (2013), Statistical Modeling and Inference for Spatio-Temporal Extremes, Ph.D. thesis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland [PhD thesis][PDF]

    • ⇒Lambert Award 2015 (Prize to recognize the work of young statisticians up to age 35)

    • ⇒EPFL Doctoral Award 2014 (2 laureates among 403 Ph.D. theses defended)