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A simultaneous estimation and variable selection rule
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DOI：10.1016/S0304-4076(00)00078-6.png)
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A new data-based method of estimation and variable selection in linear statistical models is proposed. This method is based on a generalized maximum entropy formalism, and makes use of both sample and non-sample information in determining a basis for coefficient shrinkage and extraneous variable identification. In contrast to tradition, shrinkage and variable selection are achieved on a coordinate-by-coordinate basis, and the procedure works well for both ill- and well-posed statistical models. Analytical asymptotic results are presented and sampling experiments are used as a basis for determining finite sample behavior and comparing the sampling performance of the new estimation rule with traditional competitors. Solution algorithms for the non-linear inversion problem that results are simple to implement. (C) 2001 Elsevier Science S.A. All rights reserved. JEL classification: C13; C14; C5.
Keyword:
shrinkage estimator
maximum entropy
extraneous variables
squared error loss
data weighted prior
subset selection
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