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Minimax Empirical Bayes Ridge-Principal Component Regression Estimators

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  • Tatsuya Kubokawa

    (Faculty of Economics, University of Tokyo)

  • M. S. Srivastava

    (University of Toronto)

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

Suggested Citation

  • Tatsuya Kubokawa & M. S. Srivastava, 2002. "Minimax Empirical Bayes Ridge-Principal Component Regression Estimators," CIRJE F-Series CIRJE-F-170, CIRJE, Faculty of Economics, University of Tokyo.
    • Handle: RePEc:tky:fseres:2002cf170
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    References listed on IDEAS

    as
    1. Feenstra, Robert C, 1995. "Exact Hedonic Price Indexes," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 634-653, November.
    2. Gilley, Otis W & Pace, R Kelley, 1995. "Improving Hedonic Estimation with an Inequality Restricted Estimator," The Review of Economics and Statistics, MIT Press, vol. 77(4), pages 609-621, November.
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