CRPS M-estimation for max-stable models

TL;DR

This paper introduces a new M-estimation method for max-stable models using the CRPS of multivariate CDFs, providing a tractable alternative to likelihood-based inference for multivariate extremes.

Contribution

It develops a CRPS-based M-estimation framework for max-stable models, including consistency, asymptotic normality, and practical implementation details.

Findings

Estimators are consistent and asymptotically normal.

Provides explicit formulas for asymptotic covariance matrices.

Achieves accurate confidence intervals with better coverage than existing methods.

Abstract

Max-stable random fields provide canonical models for the dependence of multivariate extremes. Inference with such models has been challenging due to the lack of tractable likelihoods. In contrast, the finite dimensional cumulative distribution functions (CDFs) are often readily available and natural to work with. Motivated by this fact, in this work we develop an M-estimation framework for max-stable models based on the continuous ranked probability score (CRPS) of multivariate CDFs. We start by establishing conditions for the consistency and asymptotic normality of the CRPS-based estimators in a general context. We then implement them in the max-stable setting and provide readily computable expressions for their asymptotic covariance matrices. The resulting point and asymptotic confidence interval estimates are illustrated over popular simulated models. They enjoy accurate coverages and offer an alternative to composite likelihood based methods.