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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- Feenstra, Robert C, 1995.
"Exact Hedonic Price Indexes,"
The Review of Economics and Statistics,
MIT Press, vol. 77(4), pages 634-53, November.
- Feenstra, R.C., 1995. "Exact Hedonic Price Indexes," Department of Economics 95-11, California Davis - Department of Economics.
- Feenstra, R.C., 1995. "Exact Hedonic Price Indexes," Papers 95-11, California Davis - Institute of Governmental Affairs.
- Robert C. Feenstra, 1995. "Exact Hedonic Price Indexes," NBER Working Papers 5061, National Bureau of Economic Research, Inc.
- 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.
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