Works by Huser, Raphaël
Results: 38
Max-and-Smooth: A Two-Step Approach for Approximate Bayesian Inference in Latent Gaussian Models.
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- Bayesian Analysis, 2021, v. 16, n. 2, p. 611, doi. 10.1214/20-BA1219
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Contributed Discussion.
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- Bayesian Analysis, 2018, v. 13, n. 3, p. 982
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Joint modelling of landslide counts and sizes using spatial marked point processes with sub-asymptotic mark distributions.
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- Journal of the Royal Statistical Society: Series C (Applied Statistics), 2023, v. 72, n. 5, p. 1139, doi. 10.1093/jrsssc/qlad077
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Geostatistical modeling to capture seismic-shaking patterns from earthquake-induced landslides.
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- Geophysical Research Abstracts, 2019, v. 21, p. 1
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Modeling soil organic carbon with Quantile Regression: Dissecting predictors' effects on carbon stocks.
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- Geophysical Research Abstracts, 2018, v. 20, p. 3086
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Proteome-level assessment of origin, prevalence and function of leucine-aspartic acid (LD) motifs.
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- Bioinformatics, 2020, v. 36, n. 4, p. 1121, doi. 10.1093/bioinformatics/btz703
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Full likelihood inference for max-stable data.
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- Stat, 2019, v. 8, n. 1, p. 1, doi. 10.1002/sta4.218
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Visuanimation in statistics.
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- Stat, 2015, v. 4, n. 1, p. 81, doi. 10.1002/sta4.77
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Flexible Modeling of Nonstationary Extremal Dependence using Spatially Fused LASSO and Ridge Penalties.
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- Technometrics, 2025, v. 67, n. 1, p. 97, doi. 10.1080/00401706.2024.2388549
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Partial Tail-Correlation Coefficient Applied to Extremal-Network Learning.
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- Technometrics, 2024, v. 66, n. 3, p. 331, doi. 10.1080/00401706.2024.2304334
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Practical strategies for generalized extreme value‐based regression models for extremes.
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- Environmetrics, 2022, v. 33, n. 6, p. 1, doi. 10.1002/env.2742
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Spatial hierarchical modeling of threshold exceedances using rate mixtures.
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- Environmetrics, 2021, v. 32, n. 3, p. 1, doi. 10.1002/env.2662
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Point process-based modeling of multiple debris flow landslides using INLA: an application to the 2009 Messina disaster.
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- Stochastic Environmental Research & Risk Assessment, 2018, v. 32, n. 7, p. 2179, doi. 10.1007/s00477-018-1518-0
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- Article
Spatial Wildfire Risk Modeling Using a Tree-Based Multivariate Generalized Pareto Mixture Model.
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- Journal of Agricultural, Biological & Environmental Statistics (JABES), 2024, v. 29, n. 2, p. 320, doi. 10.1007/s13253-023-00596-5
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A Spliced Gamma-Generalized Pareto Model for Short-Term Extreme Wind Speed Probabilistic Forecasting.
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- Journal of Agricultural, Biological & Environmental Statistics (JABES), 2019, v. 24, n. 3, p. 517, doi. 10.1007/s13253-019-00369-z
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Non-Stationary Dependence Structures for Spatial Extremes.
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- Journal of Agricultural, Biological & Environmental Statistics (JABES), 2016, v. 21, n. 3, p. 470, doi. 10.1007/s13253-016-0247-4
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Cover Image.
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- WIREs: Computational Statistics, 2022, v. 14, n. 1, p. 1, doi. 10.1002/wics.1577
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Advances in statistical modeling of spatial extremes.
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- WIREs: Computational Statistics, 2022, v. 14, n. 1, p. 1, doi. 10.1002/wics.1537
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Joint modeling and prediction of massive spatio-temporal wildfire count and burnt area data with the INLA-SPDE approach.
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- Extremes, 2023, v. 26, n. 2, p. 339, doi. 10.1007/s10687-023-00463-z
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A combined statistical and machine learning approach for spatial prediction of extreme wildfire frequencies and sizes.
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- Extremes, 2023, v. 26, n. 2, p. 301, doi. 10.1007/s10687-022-00460-8
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Modeling spatial extremes using normal mean-variance mixtures.
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- Extremes, 2022, v. 25, n. 2, p. 175, doi. 10.1007/s10687-021-00434-2
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Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes.
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- Extremes, 2021, v. 24, n. 1, p. 91, doi. 10.1007/s10687-019-00369-9
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INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles.
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- Extremes, 2018, v. 21, n. 3, p. 441, doi. 10.1007/s10687-018-0324-x
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Likelihood estimators for multivariate extremes.
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- Extremes, 2016, v. 19, n. 1, p. 79, doi. 10.1007/s10687-015-0230-4
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Space–time landslide hazard modeling via Ensemble Neural Networks.
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- Natural Hazards & Earth System Sciences, 2024, v. 24, n. 3, p. 823, doi. 10.5194/nhess-24-823-2024
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A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation.
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- Journal of the American Statistical Association, 2021, v. 116, n. 533, p. 93, doi. 10.1080/01621459.2020.1750414
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Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes.
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- Journal of the American Statistical Association, 2020, v. 115, n. 531, p. 1037, doi. 10.1080/01621459.2019.1647842
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Modeling Spatial Processes with Unknown Extremal Dependence Class.
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- Journal of the American Statistical Association, 2019, v. 114, n. 525, p. 434, doi. 10.1080/01621459.2017.1411813
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Factor Copula Models for Replicated Spatial Data.
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- Journal of the American Statistical Association, 2018, v. 113, n. 521, p. 467, doi. 10.1080/01621459.2016.1261712
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Max‐infinitely divisible models and inference for spatial extremes.
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- Scandinavian Journal of Statistics, 2021, v. 48, n. 1, p. 321, doi. 10.1111/sjos.12491
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Likelihood-Free Parameter Estimation with Neural Bayes Estimators.
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- American Statistician, 2024, v. 78, n. 1, p. 1, doi. 10.1080/00031305.2023.2249522
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Vecchia Likelihood Approximation for Accurate and Fast Inference with Intractable Spatial Max-Stable Models.
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- Journal of Computational & Graphical Statistics, 2024, v. 33, n. 3, p. 978, doi. 10.1080/10618600.2023.2285332
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Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes.
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- Journal of Computational & Graphical Statistics, 2020, v. 29, n. 1, p. 174, doi. 10.1080/10618600.2019.1647847
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High-Order Composite Likelihood Inference for Max-Stable Distributions and Processes.
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- Journal of Computational & Graphical Statistics, 2016, v. 25, n. 4, p. 1212, doi. 10.1080/10618600.2015.1086656
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Nonstationary Positive Definite Tapering On The Plane.
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- Journal of Computational & Graphical Statistics, 2013, v. 22, n. 4, p. 848, doi. 10.1080/10618600.2012.729982
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Geostatistical Modeling to Capture Seismic‐Shaking Patterns From Earthquake‐Induced Landslides.
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- Journal of Geophysical Research. Earth Surface, 2019, v. 124, n. 7, p. 1958, doi. 10.1029/2019JF005056
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Tractable Bayes of skew‐elliptical link models for correlated binary data.
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- Biometrics, 2023, v. 79, n. 3, p. 1788, doi. 10.1111/biom.13731
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Bayesian modeling of air pollution extremes using nested multivariate max‐stable processes.
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- Biometrics, 2019, v. 75, n. 3, p. 831, doi. 10.1111/biom.13051
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