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Robust and efficient estimation by minimising a density power divergence

Basu, Ayanendranath; Harris, Ian. R; Hjort, Nils L. and Jones, M. C. (1998). Robust and efficient estimation by minimising a density power divergence. Biometrika, 85(3) pp. 549–559.

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A minimum divergence estimation method is developed for robust parameter estimation. The proposed approach uses new density-based divergences which, unlike existing methods of this type such as minimum Hellinger distance estimation, avoid the use of nonparametric density estimation and associated complications such as bandwidth selection. The proposed class of ‘density power divergences’ is indexed by a single parameter α which controls the trade-off between robustness and efficiency. The methodology affords a robust extension of maximum likelihood estimation for which α = 0. Choices of α near zero afford considerable robustness while retaining efficiency close to that of maximum likelihood.

Item Type: Journal Article
Copyright Holders: 1998 Biometrika Trust
ISSN: 1464-3510
Keywords: asymptotic efficiency; influence function; M-estimation; maximum likelihood; minimum distance estimation; robustness
Academic Unit/Department: Mathematics, Computing and Technology > Mathematics and Statistics
Mathematics, Computing and Technology
Item ID: 24027
Depositing User: Sarah Frain
Date Deposited: 03 May 2011 11:58
Last Modified: 15 Jan 2016 15:03
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