Publications

  • *: Corresponding author
  • Underlined: PhD student or Postdoc advisees under my (co-)supervision at the start of main work
Under review or revision:
  • [14] Vandeskog*, S. M., Martino, S., and Huser, R. (2022+), Adjusting posteriors from composite and misspecified likelihoods with application to spatial extremes in R-INLA, arXiv preprint 2210.00760 [arXiv][PDF]

  • [13] Sainsbury-Dale*, M., Zammit-Mangion, A., and Huser, R. (2022+), Fast optimal estimation with intractable models using permutation-invariant neural networks, arXiv preprint 2208.12942 [arXiv][PDF

  • [12] 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

  • [11] Gong, Y., and Huser*, R. (2022+), Flexible modeling of multivariate spatial extremes, arXiv preprint 2206.11414 [arXiv][PDF]

  • [10] Dahal, A., Tanyas, H., van Westen, C., van der Meije, M., Mai, P. M, Huser, R., and Lombardo*, L. (2022+), Space-time landslide hazard modeling via ensemble neural networks, EarthArXiv preprint 3382 [EarthArXiv][PDF]

  •   [9] Richards*, J., and Huser, R. (2022+), A unifying partially-interpretable framework for neural network-based extreme quantile regression, arXiv preprint 2208.07581 [arXiv][PDF]

  •   [8] 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 2205.09908 [arXiv][PDF]

  •   [7] 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]

  •   [6] 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]

  •   [5] 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]

  •   [4] 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]

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

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

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

Journal papers: 
  • [47] Zhang, Z., Arellano-Valle, R. B., Genton, M. G., and Huser*, R. (2022+), Tractable Bayes of skew-elliptical link models for correlated binary data, Biometrics, to appear [journal][PDF preprint]

  • [46] 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]

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