• *: Postdocs under my (co-)supervision
  • **: PhD students under my (co-)supervision
Under review or revision:
  •   [9] Yadav**, R., Huser, R., Opitz, T., and Lombardo*, L. (2022+), Joint modeling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions, arXiv preprint 2205.XXXXX [arXiv][PDF]

  •   [8] Huser, R., Stein, M. L., and Zhong**, P. (2022+), Vecchia likelihood approximation for accurate and fast inference in intractable spatial extremes models, arXiv preprint 2203.05626 [arXiv][PDF]

  •   [7] Zhang**, Z., Krainski, E., Zhong**, P., Rue, H., and Huser, R. (2022+), Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach, arXiv preprint 2202.06502 [arXiv][PDF]

  •   [6] Cisneros**, D., Gong**, Y., Yadav**, R., Hazra*, A., and Huser, R. (2021+), A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes, arXiv preprint 2112.14920 [arXiv][PDF]

  •   [5] Hazra*, A., Huser, R., and Bolin, D. (2021+), Realistic and fast modeling of spatial extremes over large geographical domains, arXiv preprint 2112.10248 [arXiv][PDF]

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

  •   [3] Krupskii, P., and Huser, R. (2021+), Modeling spatial tail dependence with Cauchy convolution processes, arXiv preprint 2102.07094 [arXiv][PDF]

  •   [2] Zhang**, Z., Arellano-Valle, R. B., Genton, M. G., and Huser, R. (2021+), Tractable Bayes of skew-elliptical link models for correlated binary data, arXiv preprint 2101.02233 [arXiv][PDF]

  •   [1] Rubio**, R., de Carvalho, M., and Huser, R. (2017+), Similarity-based clustering for stock market extremes, in revision [PDF]

Journal papers: 
  • [46] Castro-Camilo*, D., Huser, R., and Rue, H. (2022+), Practical strategies for GEV-based regression models for extremes, Environmetrics, to appear [journal][PDF preprint]

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

  • [44] Guerrero**, M. B., Huser, R., and Ombao, H. (2022+), Conex-Connect: Learning patterns in extremal brain connectivity from multi-channel EEG data, Annals of Applied Statistics, to appear [journal][PDF preprint]

  • [43] Zhong**, P., Huser, R., and Opitz, T. (2022+), Exact simulation of max-infinitely divisible processes, Econometrics and Statistics, to appear [journal][PDF preprint]

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

  • [41] Gong**, Y., and Huser, R. (2021+), Asymmetric tail dependence modeling, with application to cryptocurrency market data, Annals of Applied Statistics, to appear [journal][PDF preprint]

  • [40] Opitz, T., Bakka, H., Huser, R., and Lombardo*, L. (2021+), High-resolution Bayesian mapping of landslide hazard with unobserved trigger event, Annals of Applied Statistics, to appear [journal][PDF preprint]

  • [39] Jóhannesson, Á. V., Siegert, S., Huser, R., Bakka, H., and Hrafnkelsson, B. (2021+), Approximate Bayesian inference for analysis of spatio-temporal flood frequency data, Annals of Applied Statistics, to appear [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, 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 modeling 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:
Book chapters:
PhD Thesis: