Modeling Spatial Processes with Unknown Extremal Dependence Class

DOI10.1080/01621459.2017.1411813zbMath1478.62277arXiv1703.06031OpenAlexW2598043207MaRDI QIDQ5229925

Raphaël Huser, Jennifer L. Wadsworth

Publication date: 19 August 2019

Published in: Journal of the American Statistical Association (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1703.06031

zbMATH Keywords

copulaspatial extremesasymptotic dependence and independencethreshold exceedancecensored likelihood inference

Mathematics Subject Classification ID

Inference from spatial processes (62M30) Censored data models (62N01) Characterization and structure theory for multivariate probability distributions; copulas (62H05) Extreme value theory; extremal stochastic processes (60G70)

Related Items (25)

Hierarchical Transformed Scale Mixtures for Flexible Modeling of Spatial Extremes on Datasets With Many Locations ⋮ Modeling nonstationary temperature maxima based on extremal dependence changing with event magnitude ⋮ Modeling spatial extremes using normal mean-variance mixtures ⋮ Linking representations for multivariate extremes via a limit set ⋮ Flexible and Fast Spatial Return Level Estimation Via a Spatially Fused Penalty ⋮ Asymmetric tail dependence modeling, with application to cryptocurrency market data ⋮ A Vecchia approximation for high-dimensional Gaussian cumulative distribution functions arising from spatial data ⋮ Joint modelling of the body and tail of bivariate data ⋮ Analysis of wildfires and their extremes via spatial quantile autoregressive model ⋮ A modeler's guide to extreme value software ⋮ High-dimensional modeling of spatial and spatio-temporal conditional extremes using INLA and Gaussian Markov random fields ⋮ Local Likelihood Estimation of Complex Tail Dependence Structures, Applied to U.S. Precipitation Extremes ⋮ Hierarchical Space-Time Modeling of Asymptotically Independent Exceedances With an Application to Precipitation Data ⋮ Estimating high-resolution red sea surface temperature hotspots, using a low-rank semiparametric spatial model ⋮ New exploratory tools for extremal dependence: \(\chi \) networks and annual extremal networks ⋮ Editorial: EVA 2019 data competition on spatio-temporal prediction of Red Sea surface temperature extremes ⋮ Basin-wide spatial conditional extremes for severe ocean storms ⋮ Extremal dependence of random scale constructions ⋮ Semiparametric bivariate modelling with flexible extremal dependence ⋮ Rank-based estimation under asymptotic dependence and independence, with applications to spatial extremes ⋮ A Spatial Markov Model for Climate Extremes ⋮ Bayesian Model Averaging Over Tree-based Dependence Structures for Multivariate Extremes ⋮ A horse race between the block maxima method and the peak-over-threshold approach ⋮ A Hierarchical Max-Infinitely Divisible Spatial Model for Extreme Precipitation ⋮ Modeling spatial tail dependence with Cauchy convolution processes

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Efficient inference and simulation for elliptical Pareto processes
• Likelihood estimators for multivariate extremes
• The generalized Pareto process; with a view towards application and simulation
• Geostatistics of dependent and asymptotically independent extremes
• Hidden regular variation, second order regular variation and asymptotic independence
• Multivariate generalized Pareto distributions: parametrizations, representations, and properties
• Dependence modelling for spatial extremes
• Max-stable models for multivariate extremes
• Statistics for near independence in multivariate extreme values
• The Stationary Bootstrap
• Markov chain models for threshold exceedances
• The multivariate skew-normal distribution
• Models and inference for uncertainty in extremal dependence
• High-dimensional peaks-over-threshold inference
• Factor Copula Models for Replicated Spatial Data
• Likelihood-Based Inference for Max-Stable Processes
• Modelling Across Extremal Dependence Classes
• Estimation of Hüsler–Reiss Distributions and Brown–Resnick Processes
• Efficient inference for spatial extreme value processes associated to log-Gaussian random functions
• Dependence measures for extreme value analyses
• Statistical modeling of spatial extremes
• Discussion of ``Statistical modeling of spatial extremes by A. C. Davison, S. A. Padoan and M. Ribatet

This page was built for publication: Modeling Spatial Processes with Unknown Extremal Dependence Class