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Activity Number:
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94
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Type:
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Invited
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Date/Time:
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Monday, August 3, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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IMS
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| Abstract - #302897 |
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Title:
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Variable Selection for Partially Linear Models with Measurement Errors
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Author(s):
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Runze Li*+
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Companies:
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Penn State University
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Address:
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Dept. of Statistics, University Park, PA, 16802-2111,
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Keywords:
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Errors-in-variable ; Local linear regression ; Quantile regression ; SCAD
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Abstract:
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This article focuses on variable selection for partially linear models when the covariates are measured with additive errors. We propose two classes of variable selection procedures, penalized least squares and penalized quantile regression, using the nonconvex penalized principle. The first procedure corrects the bias in the loss function caused by the measurement error by applying the so-called correction-for-attenuation approach, whereas the second procedure corrects the bias by using orthogonal regression. The sampling properties for the two procedures are investigated. The rate of convergence and the asymptotic normality of the resulting estimates are established. Finite sample performance of the proposed variable selection procedures is assessed by Monte Carlo simulation studies. We further illustrate the proposed procedures by an application.
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- The address information is for the authors that have a + after their name.
- Authors who are presenting talks have a * after their name.
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