Nonparametric estimation of the conditional tail index - Inria - Institut national de recherche en sciences et technologies du numérique
Conference Papers Year : 2007

Nonparametric estimation of the conditional tail index

Abstract

We present a nonparametric family of estimators for the tail index of a Pareto-type distribution when covariate information is available. Our estimators are based on a weighted sum of the log-spacings between some selected observations. This selection is achieved through a moving window approach on the covariate domain and a random threshold on the variable of interest. Asymptotic normality is proved under mild regularity conditions and illustrated for some weight functions. Finite sample performances are presented on a real data study.

Domains

Methodology [stat.ME]
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https://inria.hal.science/hal-00987250

Submitted on : Monday, May 5, 2014-5:34:56 PM

Last modification on : Thursday, April 4, 2024-9:04:09 PM

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Dates and versions

hal-00987250 , version 1 (05-05-2014)

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  • HAL Id : hal-00987250 , version 1

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Laurent Gardes, Stéphane Girard. Nonparametric estimation of the conditional tail index. Statistical Extremes and Environmental Risk Workshop, Feb 2007, Lisbonne, Portugal. pp.47-50. ⟨hal-00987250⟩

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