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• W2022796289 abstract "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. We further demonstrate that, with proper choices of the penalty functions and the regularization parameter, the resulting estimates perform asymptotically as well as an oracle procedure as proposed by Fan and Li. Choice of smoothing parameters is also discussed. 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." @default.
• W2022796289 created "2016-06-24" @default.
• W2022796289 creator A5059723521 @default.
• W2022796289 creator A5085374974 @default.
• W2022796289 date "2009-03-01" @default.
• W2022796289 modified "2023-10-16" @default.
• W2022796289 title "Variable Selection for Partially Linear Models With Measurement Errors" @default.
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• W2022796289 doi "https://doi.org/10.1198/jasa.2009.0127" @default.
• W2022796289 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/2697854" @default.
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