Nonparametric Inference for Max-Stable Dependence
(2012) Statistical Science : a review journal — Vol. 27, n° 2, p. 193-196 (2012)
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- Abstract
- 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 extreme-value copulas and max-stable processes explicit and to review the existing nonparametric inference methods.
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Segers, J. (2012). Nonparametric Inference for Max-Stable Dependence. Statistical Science : a review journal, 27(2), 193-196. https://doi.org/10.1214/11-STS376 (Original work published 2012)