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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/8004

Title: MSE superiority of Bayes and empirical Bayes estimators in two generalized seemingly unrelated regressions
Authors: Wang, L.
Issue Date: 2008
Citation: STATISTICS & PROBABILITY LETTERS, 78(2). p. 109-117
Abstract: This paper deals with the estimation problem in a system of two seemingly unrelated regression equations where the regression parameter is distributed according to the normal prior distribution N(beta(0), sigma(2)(beta)Sigma(beta)). Resorting to the covariance adjustment technique, we obtain the best Bayes estimator of the regression parameter and prove its superiority over the best linear unbiased estimator (BLUE) in terms of the mean square error (MSE) criterion. Also, under the MSE criterion, we show that the empirical Bayes estimator of the regression parameter is better than the Zellner type estimator when the covariance matrix of error variables is unknown. (c) 2007 Elsevier B.V. All rights reserved.
Notes: Jiao Tong Univ, Dept Math, Beijing 100044, Peoples R China. Hasselt Univ, Ctr Stat, B-3590 Diepenbeek, Belgium.Wang, L, Jiao Tong Univ, Dept Math, Beijing 100044, Peoples R China.wlc@amss.ac.cn
URI: http://hdl.handle.net/1942/8004
Link to publication: http/dx.doi.org/10.1016/j.spl.2007.05.008
ISI #: 000252908500002
ISSN: 0167-7152
Category: A1
Type: Journal Contribution
Validation: ecoom, 2009
Appears in Collections: Research publications

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