Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:132

Article

Tian, Ruiqin¹  Xue, Liugen¹  Liu, Chunling² 

[1] Beijing Univ Technol, Coll Appl Sci, Beijing 100124, Peoples R China

[2] Hong Kong Polytech Univ, Dept Appl Math, Hong Kong, Hong Kong, Peoples R China

关键词: Semiparametric varying coefficient partially linear models;    Variable selection;    Longitudinal data;    Quadratic inference functions;   

DOI  :  10.1016/j.jmva.2014.07.015

来源: Elsevier

【 摘 要 】

In this paper, we focus on the variable selection for semiparametric varying coefficient partially linear models with longitudinal data. A new variable selection procedure is proposed based on the combination of the basis function approximations and quadratic inference functions. The proposed procedure simultaneously selects significant variables in the parametric components and the nonparametric components. With appropriate selection of the tuning parameters, we establish the consistency and asymptotic normality of the resulting estimators. Extensive Monte Carlo simulation studies are conducted to examine the finite sample performance of the proposed variable selection procedure. We further illustrate the proposed procedure by an application. (C) 2014 Elsevier Inc. All rights reserved.

【 授权许可】

Free