Penalized estimating functions and variable selection in semiparametric regression models

• 成果类型: Article
• 署名作者: Johnson, Brent A.; Lin, D. Y.; Zeng, Donglin
• 署名单位: Emory University; University of North Carolina; University of North Carolina Chapel Hill
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000184
• 发表日期: 2008
• 页码: 672-680
• 关键词: LONGITUDINAL DATA-ANALYSIS; right-censored data; linear rank-tests; oracle properties; LARGE-SAMPLE; Lasso

摘要

We propose a general strategy for variable selection in semiparametric regression models by penalizing appropriate estimating functions. Important applications include semiparametric linear regression with censored responses and semiparametric regression with missing predictors. Unlike the existing penalized maximum likelihood estimators, the proposed penalized estimating functions may not pertain to the derivatives of any objective functions and may be discrete in the regression coefficients. We establish a general asymptotic theory for penalized estimating functions and present suitable numerical algorithms to implement the proposed estimators. In addition, we develop a resampling technique to estimate the variances of the estimated regression coefficients when the asymptotic variances cannot be evaluated directly. Simulation studies demonstrate that the proposed methods perform well in variable selection and variance estimation. We illustrate our methods using data from the Paul Coverdell Stroke Registry.