Improved estimation under collinearity and squared error loss
This paper examines the performance of several biased, Stein-like and empirical Bayes estimators for the general linear statistical model under conditions of collinearity. A new criterion for deleting principal components, based on an unbiased estimator of risk, is introduced. Using a squared error measure and Monte Carlo sampling experiments, the resulting estimator's performance is evaluated and compared with other traditional and non-traditional estimators.
Year of publication: |
1990
|
---|---|
Authors: | Hill, R. Carter ; Judge, George G |
Published in: |
Journal of Multivariate Analysis. - Elsevier, ISSN 0047-259X. - Vol. 32.1990, 2, p. 296-312
|
Publisher: |
Elsevier |
Keywords: | multicollinearity principal components linear regression Stein rules empirical Bayes estimators unbiased estimation of risk |
Saved in:
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