SSP'05 -- Proceedings

	IEEE/SP 13th workshop on Statistical Signal Processing July, 17-20, 2005 - Bordeaux - France

Information regarding the paper

Title

Semiparametric Model Selection With Applications to Regression

Author(s)

Zhanlue Zhao	University of New Orleans
Huimin Chen	University of New Orleans
X.Rong Li	University of New Orleans

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Abstract

In this paper we consider model selection problem using samples of small or moderate size where each model can have unknown parameter without a fully speci- ed likelihood function. A semiparametric model selection criterion is proposed where the penalty-based model complexity term is used for the parameter with fully specified model structure and the kernel density estimation is used for the unknown noise distribution. A linear regression problem with various noise distributions is studied and the numerical results reveal that the semiparametric approach outperforms the penalty-based criteria and cross validation.