Estimating the endpoint of a distribution in the presence of additive observation errors

A. Goldenshluger; A. Tsybakov

doi:10.1016/j.spl.2004.01.013

Estimating the endpoint of a distribution in the presence of additive observation errors

A. Goldenshluger, A. Tsybakov

Department of Statistics

Research output: Contribution to journal › Article › peer-review

Abstract

We consider the problem of estimating the endpoint of a probability distribution in the presence of observation errors, when the available sample is drawn from the convolution with some error density. We study the cases of Gaussian errors and errors with bounded support, and propose estimators that are optimal in a minimax sense.

Original language	English
Pages (from-to)	39-49
Number of pages	11
Journal	Statistics and Probability Letters
Volume	68
Issue number	1
DOIs	https://doi.org/10.1016/j.spl.2004.01.013
State	Published - 1 Jun 2004

Keywords

Deconvolution
Estimation of support of a probability density
Extreme value distribution
Optimal rates of convergence

ASJC Scopus subject areas

Statistics and Probability
Statistics, Probability and Uncertainty

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10.1016/j.spl.2004.01.013

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abstract = "We consider the problem of estimating the endpoint of a probability distribution in the presence of observation errors, when the available sample is drawn from the convolution with some error density. We study the cases of Gaussian errors and errors with bounded support, and propose estimators that are optimal in a minimax sense.",

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KW - Estimation of support of a probability density

KW - Extreme value distribution

KW - Optimal rates of convergence

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Estimating the endpoint of a distribution in the presence of additive observation errors

Abstract

Keywords

ASJC Scopus subject areas

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