Publications (18 published, 9 under review, total: 27)

 

*: Postdocs under my (co-)supervision

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

 

Statistical Methodology & Computing (12 published/accepted, 6 under review, total: 18):

[S18]  Huser, R., Opitz, T., and Thibaud, E. (2017+), Penultimate modeling of spatial extremes: statistical inference for max-infinitely divisible processes, Submitted, arXiv preprint 1801.02946

[S17]  Castro Camilo*, D., and Huser, R. (2017+), Local likelihood estimation of complex tail dependence structures in high dimensions, applied to U.S. precipitation extremes, Submitted, arXiv preprint 1710.00875

[S16]  Hofert, M., Huser, R., and Prasad, A. (2017+), Nested and hierarchical archimax copulas, Submitted, arXiv preprint 1707.00517

[S15]  Vettori**, S., Huser, R., Segers, J., and Genton, M. G. (2017+), Bayesian model averaging over tree-based dependence structures for multivariate extremes, Submitted, arXiv preprint 1705.10488

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

[S14]  Rubio**, R., de Carvalho, M., and Huser, R. (2017+), Similarity-based clustering for stock market extremes, Submitted

[S13]  Dombry, C., Genton, M. G., Huser, R., and Ribatet, M. (2017+), Full likelihood inference for max-stable data, Submitted, arXiv preprint 1703.08665

[S12]  Huser, R. and Wadsworth, J. (2017+), Modeling spatial processes with unknown extremal dependence class, Journal of the American Statistical Association – Theory and Methods, to appear

[S11]  Vettori**, S., Huser, R., and Genton, M. G. (2017+), A comparison of dependence function estimators in multivariate extremes, Statistics and Computing, to appear

[S10]  Krupskii, P., Huser, R., and Genton, M. G. (2017+), Factor copula models for replicated spatial data, Journal of the American Statistical Association – Theory and Methods, to appear

  [S9]  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

  [S8]  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

  [S7]  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

  [S6] 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

   [S5]  Huser, R., Davison, A. C., and Genton, M. G. (2016), Likelihood estimators for multivariate extremes, Extremes 19, 79-103

   [S4]  Genton, M. G., Castruccio, S., Crippa, P., Dutta, S., Huser, R., Sun, Y., and Vettori, S. (2015), Visuanimation in statistics, Stat 4, 81-96

   [S3]  Huser, R., and Davison, A. C. (2014), Space-time modeling of extreme events, Journal of the Royal Statistical Society – Series B 76, 439-461

   [S2]  Huser, R., and Davison, A. C. (2013), Composite likelihood estimation for the Brown-Resnick process, Biometrika 100, 511-518

   [S1]  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

 

Interdisciplinary collaborations (3 published/accepted, 2 under review, total: 5):

   [I5]  Alam, T., Alazmi, M., Naser, R., Huser, F., Walkiewicz, K. W., Canlas, C. G., Huser, R., Ali, A. J., Merzaban, J., Bajic, V. B., Gao, X., and Arold, S. T. (2017+), Proteome-level assessment of prevalence and function of Leucine-Aspartic Acid (LD) motifs, Submitted.    

   [I4]  Lombardo*, L., Opitz, T., and Huser, R. (2017+), Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster, Submitted, arXiv preprint 1708.03156

   [I3]  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, to appear.

   [I2]  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

   [I1]  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

 

Other material: Reviews, Book Chapters, and PhD Thesis (3 published/accepted, 1 under review, total: 4):

  [O4]  Davison, A. C., Huser, R., and Thibaud, E. (2017+), Spatial Extremes, In Handbook of Environmental and Ecological Statistics, editors A. E. Gelfand, M. Fuentes, J. A. Hoeting and R. L. Smith. CRC Press, Under review

  [O3]  Davison, A. C., and Huser, R. (2015), Statistics of Extremes, Annual Review of Statistics and its Application 2, 203-235

  [O2]  Davison, A. C., Huser, R., and Thibaud, E. (2013), Geostatistics of dependent and asymptotically independent extremes, Mathematical Geosciences 45, 511-529

  [O1]  Huser, R. (2013), Statistical Modeling and Inference for Spatio-Temporal Extremes, Ph.D. thesis, École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland