期刊
JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
卷 104, 期 485, 页码 234-248出版社
TAYLOR & FRANCIS INC
DOI: 10.1198/jasa.2009.0127
关键词
Errors-in-variable; Error-free; Error-prone; Local linear regression; Quantile regression; smoothly clipped absolute deviation
资金
- NIH/NIAID [A162247, A159773]
- NSF [DMS-0806097, DMS-034886]
- National Institute on Drug Abuse (NIDA) [P50 DA10075]
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 property. 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.
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