Likelihood-based inference for max-stable processes

2009/02/18 by Simone A. Padoan, Padoan, Simone A., Mathieu Ribatet +3 · 5 citations
Mathematics · Social Sciences · #FOS: Computer and information sciences #Insurance, Mortality, Demography, Risk Management #Methodology (stat.ME) #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · doi:10.48550/arxiv.0902.3060

openalex publication_date 2009/02/18 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

The last decade has seen max-stable processes emerge as a common tool for the statistical modeling of spatial extremes. However, their application is complicated due to the unavailability of the multivariate density function, and so likelihood-based methods remain far from providing a complete and flexible framework for inference. In this article we develop inferentially practical, likelihood-based methods for fitting max-stable processes derived from a composite-likelihood approach. The procedure is sufficiently reliable and versatile to permit the simultaneous modeling of marginal and dependence parameters in the spatial context at a moderate computational cost. The utility of this methodology is examined via simulation, and illustrated by the analysis of U.S. precipitation extremes.

Citations

• Limiting forms of the frequency distribution of the largest or smallest member of a sample

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• Amortized Bayesian Inference for Spatio-Temporal Extremes: A Copula Factor Model with Autoregression
• A penalized least squares estimator for extreme-value mixture models

Related

• High-order Composite Likelihood Inference for Max-Stable Distributions\n and Processes
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• Conditional simulation of max-stable processes