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May, 1977 Minimum Hellinger Distance Estimates for Parametric Models
Rudolf Beran
Ann. Statist. 5(3): 445-463 (May, 1977). DOI: 10.1214/aos/1176343842

Abstract

This paper defines and studies for independent identically distributed observations a new parametric estimation procedure which is asymptotically efficient under a specified regular parametric family of densities and is minimax robust in a small Hellinger metric neighborhood of the given family. Associated with the estimator is a goodness-of-fit statistic which assesses the adequacy of the chosen parametric model. The fitting of a normal location-scale model by the new procedure is exhibited numerically on clear and on contaminated data.

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Rudolf Beran. "Minimum Hellinger Distance Estimates for Parametric Models." Ann. Statist. 5 (3) 445 - 463, May, 1977. https://doi.org/10.1214/aos/1176343842

Information

Published: May, 1977
First available in Project Euclid: 12 April 2007

zbMATH: 0381.62028
MathSciNet: MR448700
Digital Object Identifier: 10.1214/aos/1176343842

Subjects:
Primary: 62G35
Secondary: 62F10

Keywords: asymptotically efficient estimates , minimax robust , minimum Hellinger distance estimates , Robust estimates

Rights: Copyright © 1977 Institute of Mathematical Statistics

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Vol.5 • No. 3 • May, 1977
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