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Estimators for the linear regression model based on Winsorized observations

Estimators for the linear regression model based on Winsorized observations
Estimators for the linear regression model based on Winsorized observations
We develop an asymptotic, robust version of the Gauss-Markov theorem for estimating the regression parameter vector ?? and a parametric function ?? in the linear regression model. In a class of estimators for estimating ?? that are linear in a Winsorized observation vector introduced by Welsh (1987), we show that Welsh's trimmed mean has smallest asymptotic covariance matrix. Also, for estimating a parametric function ??, the inner product of ? and the trimmed mean has the smallest asymptotic variance among a class of estimators linear in the Winsorized observation vector. A generalization of the linear Winsorized mean to the multivariate context is also given. Examples analyzing American lobster data and the mineral content of bones are used to compare the robustness of some trimmed mean methods.
linear regression, robust estimation, trimmed mean, winsorized mean
1017-0405
31-53
Chen, L-A.
65cb8d8e-b9b2-420d-95bc-fe28df8ea2b4
Welsh, A.H.
27640871-afff-4d45-a191-8a72abee4c1a
Chan, W.
27980e6d-0f21-4395-9801-45c0d81984f4
Chen, L-A.
65cb8d8e-b9b2-420d-95bc-fe28df8ea2b4
Welsh, A.H.
27640871-afff-4d45-a191-8a72abee4c1a
Chan, W.
27980e6d-0f21-4395-9801-45c0d81984f4

Chen, L-A., Welsh, A.H. and Chan, W. (2001) Estimators for the linear regression model based on Winsorized observations. Statistica Sinica, 11 (1), 31-53.

Record type: Article

Abstract

We develop an asymptotic, robust version of the Gauss-Markov theorem for estimating the regression parameter vector ?? and a parametric function ?? in the linear regression model. In a class of estimators for estimating ?? that are linear in a Winsorized observation vector introduced by Welsh (1987), we show that Welsh's trimmed mean has smallest asymptotic covariance matrix. Also, for estimating a parametric function ??, the inner product of ? and the trimmed mean has the smallest asymptotic variance among a class of estimators linear in the Winsorized observation vector. A generalization of the linear Winsorized mean to the multivariate context is also given. Examples analyzing American lobster data and the mineral content of bones are used to compare the robustness of some trimmed mean methods.

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More information

Published date: 2001
Keywords: linear regression, robust estimation, trimmed mean, winsorized mean
Organisations: Statistics

Identifiers

Local EPrints ID: 29931
URI: http://eprints.soton.ac.uk/id/eprint/29931
ISSN: 1017-0405
PURE UUID: 2cf4bb41-3b98-4859-8d4d-96fdd1313884

Catalogue record

Date deposited: 11 May 2006
Last modified: 08 Jan 2022 06:54

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Contributors

Author: L-A. Chen
Author: A.H. Welsh
Author: W. Chan

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