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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)

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    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 ~ q known matrix C, q

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    File URL: http://www.cirje.e.u-tokyo.ac.jp/research/dp/2002/2002cf170.pdf
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    Bibliographic Info

    Paper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-170.

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    Length: 31 pages
    Date of creation: Sep 2002
    Date of revision:
    Handle: RePEc:tky:fseres:2002cf170

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    1. 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 609-21, November.
    2. Feenstra, R.C., 1995. "Exact Hedonic Price Indexes," Department of Economics 95-11, California Davis - Department of Economics.
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