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Activity Number:
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362
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Type:
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Invited
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Date/Time:
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Wednesday, August 1, 2007 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Teaching Statistics in the Health Sciences
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| Abstract - #307695 |
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Title:
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Variable Selection in Semiparametric Regression Modeling
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Author(s):
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Runze Li*+ and Hua Liang
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Companies:
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The Pennsylvania State University and University of Rochester
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Address:
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Department of Statistics, University Park, PA, 16802-2111,
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Keywords:
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Nonconcave penalized likelihood ; SCAD ; efficient score ; local linear regression ; partially linear model ; varying coefficient models
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Abstract:
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We propose a class of variable selection procedures for semiparametric regression models using nonconcave penalized likelihood. The proposed procedures are distinguished from the traditional ones in that they delete insignificant variables and estimate the coefficients of significant variables simultaneously. With proper choices of penalty functions and regularization parameters, we establish the asymptotic normality of the resulting estimate, and further demonstrate that the proposed procedures perform as well as an oracle procedure. Semiparametric generalized likelihood ratio test is proposed to select significant variables in the nonparametric component. We investigate the asymptotic behavior of the proposed test and demonstrate its limiting null distribution follows a chi-squared distribution, which is independent of the nuisance parameters.
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