All Block Maxima method for estimating the extreme value index

Fuente: arXiv
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Hauptverfasser: Oorschot, Jochem, Zhou, Chen
Format: Preprint
Veröffentlicht: 2020
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author Oorschot, Jochem
Zhou, Chen
author_facet Oorschot, Jochem
Zhou, Chen
contents The block maxima (BM) approach in extreme value analysis fits a sample of block maxima to the Generalized Extreme Value (GEV) distribution. We consider all potential blocks from a sample, which leads to the All Block Maxima (ABM) estimator. Different from existing estimators based on the BM approach, the ABM estimator is permutation invariant. We show the asymptotic behavior of the ABM estimator, which has the lowest asymptotic variance among all estimators using the BM approach. Simulation studies justify our asymptotic theories. A key step in establishing the asymptotic theory for the ABM estimator is to obtain asymptotic expansions for the tail empirical process based on higher order statistics with weights.
format Preprint
id arxiv_https___arxiv_org_abs_2010_15950
institution arXiv
publishDate 2020
record_format arxiv
spellingShingle All Block Maxima method for estimating the extreme value index
Oorschot, Jochem
Zhou, Chen
Statistics Theory
The block maxima (BM) approach in extreme value analysis fits a sample of block maxima to the Generalized Extreme Value (GEV) distribution. We consider all potential blocks from a sample, which leads to the All Block Maxima (ABM) estimator. Different from existing estimators based on the BM approach, the ABM estimator is permutation invariant. We show the asymptotic behavior of the ABM estimator, which has the lowest asymptotic variance among all estimators using the BM approach. Simulation studies justify our asymptotic theories. A key step in establishing the asymptotic theory for the ABM estimator is to obtain asymptotic expansions for the tail empirical process based on higher order statistics with weights.
title All Block Maxima method for estimating the extreme value index
topic Statistics Theory
url https://arxiv.org/abs/2010.15950