Minimax Empirical Bayes Ridge-Principal Component Regression Estimators
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
|Date of creation:||Sep 2002|
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- Robert C. Feenstra, 1995.
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