Estimators for the linear regression model based on Winsorized observations
Chen, LA., Welsh, A.H. and Chan, W. (2001) Estimators for the linear regression model based on Winsorized observations. Statistica Sinica, 11, (1), 3153.
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Description/Abstract
We develop an asymptotic, robust version of the GaussMarkov 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.
Item Type:  Article  

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Keywords:  linear regression, robust estimation, trimmed mean, winsorized mean  
Subjects:  Q Science > QA Mathematics H Social Sciences > HA Statistics 

Divisions:  University Structure  Pre August 2011 > School of Mathematics > Statistics 

ePrint ID:  29931  
Date : 


Date Deposited:  11 May 2006  
Last Modified:  31 Mar 2016 11:56  
URI:  http://eprints.soton.ac.uk/id/eprint/29931 
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