Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/36077
Title: Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data
Authors: Tian, RQ
Xue, LG
Liu, CL 
Keywords: Semiparametric varying coefficient partially linear models
Variable selection
Longitudinal data
Quadratic inference functions
Issue Date: 2014
Publisher: Academic Press
Source: Journal of multivariate analysis, 2014, v. 132, p. 94-110 How to cite?
Journal: Journal of multivariate analysis 
Abstract: 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.
URI: http://hdl.handle.net/10397/36077
ISSN: 0047-259X
DOI: 10.1016/j.jmva.2014.07.015
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