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

TIAN Ruiqin; XUE Liugen

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

TIAN Ruiqin 1,XUE Liugen 2

1.College of Applied Science, Beijing University of Technology, Beijing 100124

2.College of Applied Sciences, Beijing University of Technology, Beijing 100124

*Correspondence author

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Funding: theBeijing Municipal Education Commission Foundation(KM201110005029) and the PhD Program Foundation of Ministry of Education of China （No.20121103110004）, the Science and Technology Project of the Faculty Adviser of Excellent PhD Degree Thesis of Beijing （No.20111000503）

Opened online: 4 December 2013

Accepted by: none

Citation: TIAN Ruiqin,XUE Liugen.Penalized quadratic inference functions for semiparametric varying coefficient partially linear models with longitudinal data[OL]. [ 4 December 2013] http://en.paper.edu.cn/en_releasepaper/content/4572222

In this paper, we focus on the variable selections forsemiparametric varying coefficient partially linear models withlongitudinal data. A new variable selection procedure is proposedbased on the combination of the basis function approximations andquadratic inference functions. The proposed procedure simultaneouslyselects significant variables in the parametric components and thenonparametric components. With appropriate selection of the tuningparameters, we establish the consistency and asymptotic normality ofthe resulting estimators. Extensive Monte Carlo simulation studiesare conducted to examine the finite sample performance of theproposed variable selection procedures. We further illustrate theproposed procedures by an application.

Keywords:Semiparametric varying coefficient partially linear models; Variable selection; Longitudinal data; Quadratic inference function.

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