*: Postdocs under my (co-)supervision

**: PhD students under my (co-)supervision


Journal papers:

[20] 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, to appear

[19] Huser, R. and Wadsworth, J. (2018+), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association – Theory and Methods, to appear

[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

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

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

[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

[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

[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

[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

[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

[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

[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

       -> 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

[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

[6] Davison, A. C., and Huser, R. (2015), Statistics of Extremes, Annual Review of Statistics and its Application 2, 203-235

[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

[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

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

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

[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


Contributions to papers with discussion:

[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, to appear

Book chapters:

[2] Lombardo*, L., Opitz, T., and Huser, R. (2018+), Numerical recipies 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, Accepted

[1] Davison, A. C., Huser, R., and Thibaud, E. (2018+), Spatial extremes, In Handbook of Environmental and Ecological Statistics, editors A. E. Gelfand, M. Fuentes, J. A. Hoeting and R. L. Smith, CRC Press, Accepted

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

       -> 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)

Under review:

- Lombardo*, L., Bakka, H., Tanyas, H., van Westen, C., Mai, P. M., and Huser, R. (2018+), Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides, arXiv preprint 1807.08513

- Huser, R., Opitz, T., and Thibaud, E. (2018+), Max-infinitely divisible models and inference for spatial extremes, arXiv preprint 1801.02946

- Castro Camilo*, D., and Huser, R. (2018+), Local likelihood estimation of complex tail dependence structures, applied to U.S. precipitation extremes, arXiv preprint 1710.00875

- Huser, R., Dombry, C., Ribatet, M., and Genton, M. G. (2018+), Full likelihood inference for max-stable data, arXiv preprint 1703.08665

- Vettori**, S., Huser, R., Segers, J., and Genton, M. G. (2018+), Bayesian model averaging over tree-based dependence structures for multivariate extremes, arXiv preprint 1705.10488

       -> ENVR Student Paper Award 2017, Section on Statistics and the Environment, ASA

- Bopp, G., Shaby, B., and Huser, R. (2018+), A hierarchical max-infinitely divisible process for extreme areal precipitation over watersheds, arXiv preprint 1805.06084

- Vettori**, S., Huser, R., and Genton, M. G. (2018+), Bayesian modeling of air pollution extremes using nested multivariate max-stable processes, arXiv preprint 1804.04588

       -> Distinguished Student Paper Award 2018, Eastern North American Region (ENAR) of the International Biometric Society​

- Rubio**, R., de Carvalho, M., and Huser, R. (2017+), Similarity-based clustering for stock market extremes

- Alam, T., Alazmi, M., Naser, R., Huser, F., Momin, A. A., Walkiewicz, K. W., Canlas, C. G., Huser, R., Ali, A., Merzaban, J., Bajic, V. B., Gao, X., and Arold, S. T. (2017+), Proteome-level assessment of origin, prevalence and function of Leucine-Aspartic Acid (LD) motifs, bioRxiv preprint dio:10.1101/278903