Basu, Ayanendranath; Harris, Ian. R; Hjort, Nils L. and Jones, M. C.
|DOI (Digital Object Identifier) Link:||http://doi.org/10.1093/biomet/85.3.549|
|Google Scholar:||Look up in Google Scholar|
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|
|Keywords:||asymptotic efficiency; influence function; M-estimation; maximum likelihood; minimum distance estimation; robustness|
|Academic Unit/Department:||Faculty of Science, Technology, Engineering and Mathematics (STEM) > Mathematics and Statistics
Faculty of Science, Technology, Engineering and Mathematics (STEM)
|Depositing User:||Sarah Frain|
|Date Deposited:||03 May 2011 11:58|
|Last Modified:||04 Oct 2016 10:47|
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