Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data

Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference fun...

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Main Authors: Jinghua Zhang, Liugen Xue
Format: Article
Language:English
Published: Hindawi Limited 2020-01-01
Series:Computational and Mathematical Methods in Medicine
Online Access:http://dx.doi.org/10.1155/2020/3505306
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spelling doaj-f623251977e641a4ab9e4b39d9422a4b2020-11-25T03:56:37ZengHindawi LimitedComputational and Mathematical Methods in Medicine1748-670X1748-67182020-01-01202010.1155/2020/35053063505306Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal DataJinghua Zhang0Liugen Xue1Department of Information Engineering, Jingdezhen Ceramic Institute, Jiangxi, ChinaCollege of Applied Sciences, Beijing University of Technology, Beijing, ChinaSemiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. 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, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.http://dx.doi.org/10.1155/2020/3505306
collection DOAJ
language English
format Article
sources DOAJ
author Jinghua Zhang
Liugen Xue
spellingShingle Jinghua Zhang
Liugen Xue
Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
Computational and Mathematical Methods in Medicine
author_facet Jinghua Zhang
Liugen Xue
author_sort Jinghua Zhang
title Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
title_short Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
title_full Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
title_fullStr Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
title_full_unstemmed Penalized Quadratic Inference Function-Based Variable Selection for Generalized Partially Linear Varying Coefficient Models with Longitudinal Data
title_sort penalized quadratic inference function-based variable selection for generalized partially linear varying coefficient models with longitudinal data
publisher Hindawi Limited
series Computational and Mathematical Methods in Medicine
issn 1748-670X
1748-6718
publishDate 2020-01-01
description Semiparametric generalized varying coefficient partially linear models with longitudinal data arise in contemporary biology, medicine, and life science. In this paper, we consider a variable selection procedure based on the combination of the basis function approximations and quadratic inference functions with SCAD penalty. 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, sparsity, and asymptotic normality of the resulting estimators. The finite sample performance of the proposed methods is evaluated through extensive simulation studies and a real data analysis.
url http://dx.doi.org/10.1155/2020/3505306
work_keys_str_mv AT jinghuazhang penalizedquadraticinferencefunctionbasedvariableselectionforgeneralizedpartiallylinearvaryingcoefficientmodelswithlongitudinaldata
AT liugenxue penalizedquadraticinferencefunctionbasedvariableselectionforgeneralizedpartiallylinearvaryingcoefficientmodelswithlongitudinaldata
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