Variable Selection in Nonparametric Varying-Coefficient Models for Analysis of Repeated Measurements

• 成果类型: Article
• 署名作者: Wang, Lifeng; Li, Hongzhe; Huang, Jianhua Z.
• 署名单位: University of Pennsylvania; Texas A&M University System; Texas A&M University College Station
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000788
• 发表日期: 2008
• 页码: 1556-1569
• 关键词: nonconcave penalized likelihood; longitudinal data-analysis; large covariance matrices; component selection; spline estimation; cell-cycle; regression; regularization; inference

摘要

Nonparametric varying-coefficient models are commonly used for analyzing data measured repeatedly over time, including longitudinal and functional response data. Although many procedures have been developed for estimating varying coefficients. the problem of variable selection for such models has rot been addressed to date. la this article we present a regularized estimation procedure for variable selection that combines basis function approximations and the smoothly clipped absolute deviation penalty. The proposed I)procedure Sill)simultaneously selects significant variables with time-varying, effects and estimates the nonzero smooth coefficient functions. Under suitable conditions. we establish the theoretical properties of our procedure, including consistency in variable selection and the oracle property in estimation. Here the oracle property means that the asymptotic distribution of an estimated coefficient function is the same as that when it is known a priori which variables are in the model. The method is illustrated with simulations and tow real data examples, one for identifying risk factors in the study of AIDS and one using microarray time-course gene expression data to identify the transcription factors related to the yeast cell-cycle process.