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2013 Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression
Abhik Ghosh, Ayanendranath Basu
Electron. J. Statist. 7: 2420-2456 (2013). DOI: 10.1214/13-EJS847

Abstract

In real life we often have to deal with situations where the sampled observations are independent and share common parameters in their distribution but are not identically distributed. While the methods based on maximum likelihood provide canonical approaches for doing statistical inference in such contexts, it carries with it the usual baggage of lack of robustness to small deviations from the assumed conditions. In the present paper we develop a general estimation method for handling such situations based on a minimum distance approach which exploits the robustness properties of the density power divergence measure (Basu et al. 1998 [2]). We establish the asymptotic properties of the proposed estimators, and illustrate the benefits of our method in case of linear regression.

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Abhik Ghosh. Ayanendranath Basu. "Robust estimation for independent non-homogeneous observations using density power divergence with applications to linear regression." Electron. J. Statist. 7 2420 - 2456, 2013. https://doi.org/10.1214/13-EJS847

Information

Published: 2013
First available in Project Euclid: 2 October 2013

zbMATH: 1349.62087
MathSciNet: MR3117102
Digital Object Identifier: 10.1214/13-EJS847

Subjects:
Primary: 62F35
Secondary: 62J05

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society

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