Asymptotically Unbiased Estimation of the Coefficient of Tail Dependence
We introduce and study a class of weighted functional estimators for the coefficient of tail dependence in bivariate extreme value statistics. Asymptotic normality of these estimators is established under a second-order condition on the joint tail behaviour, some conditions on the weight function an...
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Veröffentlicht in: | Scandinavian journal of statistics 2013-03, Vol.40 (1), p.174-189 |
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container_title | Scandinavian journal of statistics |
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creator | GOEGEBEUR, YURI GUILLOU, ARMELLE |
description | We introduce and study a class of weighted functional estimators for the coefficient of tail dependence in bivariate extreme value statistics. Asymptotic normality of these estimators is established under a second-order condition on the joint tail behaviour, some conditions on the weight function and for appropriately chosen sequences of intermediate order statistics. Asymptotically unbiased estimators are constructed by judiciously chosen linear combinations of weighted functional estimators, and variance optimality within this class of asymptotically unbiased estimators is discussed. The finite sample performance of some specific examples from our class of estimators and some alternatives from the recent literature are evaluated with a small simulation experiment. |
doi_str_mv | 10.1111/j.1467-9469.2012.00800.x |
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subjects | Asymptotic properties bias-correction coefficient of tail dependence Distribution functions Estimating techniques Estimation bias Estimators Mathematics Maximum likelihood estimation multivariate extremes Sampling bias second-order condition Simulations Statism Statistical estimation Statistical methods Statistical variance Statistics Studies |
title | Asymptotically Unbiased Estimation of the Coefficient of Tail Dependence |
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