Minimax Empirical Bayes Ridge-Principal Component Regression Estimators

Tatsuya Kubokawa and M. S. Srivastava
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Tatsuya Kubokawa: Faculty of Economics, University of Tokyo
M. S. Srivastava: University of Toronto

No CIRJE-F-170, CIRJE F-Series from CIRJE, Faculty of Economics, University of Tokyo

Abstract: In this paper, we consider the problem of estimating the regression parameters in a multiple linear regression model with design matrix A when the multicollinearity is present. Minimax empirical Bayes estimators are proposed under the assumption of normality and loss function (ƒÂ-s)t (At A)2 (ƒÂ- s)/ƒÐ2, where ƒÂ is an estimator of the vector s of p regression parameters, and ƒÐ2 is the unknown variance of the model. The minimax estimators are also obtained under linear constraints on s such as s = Cƒ¿ for some p x q known matrix C, q

Pages: 31 pages
Date: 2002-09
New Economics Papers: this item is included in nep-ecm
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Persistent link: https://EconPapers.repec.org/RePEc:tky:fseres:2002cf170

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