An asymptotic expansion of the empirical angular measure for bivariate extremal dependence

Stéphane Lhaut and Johan Segers
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Stéphane Lhaut: Université catholique de Louvain, LIDAM/ISBA, Belgium
Johan Segers: Université catholique de Louvain, LIDAM/ISBA, Belgium

No 2024033, LIDAM Reprints ISBA from Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA)

Abstract: The angular measure on the unit sphere characterizes the first-order dependence structure of the components of a random vector in extreme regions and is defined in terms of standardized margins. Its statistical recovery is an important step in learning problems involving observations far away from the center. In the common situation that the components of the vector have different distributions, the rank transformation offers a convenient and robust way of standardizing data in order to build an empirical version of the angular measure based on the most extreme observations. We provide a functional asymptotic expansion for the empirical angular measure in the bivariate case based on the theory of weak convergence in the space of bounded functions. From the expansion, not only can the known asymptotic distribution of the empirical angular measure be recovered, it also enables to find expansions and weak limits for other statistics based on the associated empirical process or its quantile version.

Pages: 32
Date: 2024-11-04
Note: In: M. Barigozzi, S. Hörmann, D. Paindaveine (eds), Recent Advances in Econometrics and Statistics, Springer, 2024, p. 187-208
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Persistent link: https://EconPapers.repec.org/RePEc:aiz:louvar:2024033

DOI: 10.1007/978-3-031-61853-6_10

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