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LOCALIZED MODEL SELECTION FOR REGRESSION

Published online by Cambridge University Press:  15 January 2008

Yuhong Yang
Yuhong Yang
Affiliation:
University of Minnesota
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Abstract

Research on model/procedure selection has focused on selecting a single model globally. In many applications, especially for high-dimensional or complex data, however, the relative performance of the candidate procedures typically depends on the location, and the globally best procedure can often be improved when selection of a model is allowed to depend on location. We consider localized model selection methods and derive their theoretical properties.This research was supported by U.S. National Science Foundation CAREER Grant DMS0094323. We thank three referees and the editors for helpful comments on improving the paper.

Information

Type
Research Article
Information
Econometric Theory , Volume 24 , Issue 2 , April 2008 , pp. 472 - 492
Copyright
© 2008 Cambridge University Press

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