2012/05/01 by Johan Segers · 4 citations
Economics, Econometrics and Finance · Mathematics · #Artificial intelligence #Computer science #Econometrics #Inference #Mathematics #Nonparametric statistics #Spatial and Panel Data Analysis #Statistical Methods and Bayesian Inference #Statistical Methods and Inference #Statistical inference #Statistics #stat.ME
paper · pdf · doi:10.1214/12-sts376c
published in Statistical Science 27(2) (Institute of Mathematical Statistics) · Published in at http://dx.doi.org/10.1214/12-STS376C the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
openalex publication_date 2012/05/01 · arxiv created 2012/08/17 · arxiv updated 2012/08/20 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
The choice for parametric techniques in the discussion article is motivated by the claim that for multivariate extreme-value distributions, "owing to the curse of dimensionality, nonparametric estimation has essentially been confined to the bivariate case" (Section 2.3).Thanks to recent developments, this is no longer true if data take the form of multivariate maxima, as is the case in the article.A wide range of nonparametric, rank-based estimators and tests are nowadays available for extreme-value copulas.Since max-stable processes have extreme-value copulas, these methods are applicable for inference on max-stable processes too.The aim of this note is to make the link between extremevalue copulas and max-stable processes explicit and to review the existing nonparametric inference methods.