Unified Variable Selection for Varying Coefficient Models with Longitudinal Data

Variable selection for varying coefficient models includes the separation of varying and constant effects, and the selection of variables with nonzero varying effects and those with nonzero constant effects. This paper proposes a unified variable selection approach called the double-penalized quadra...

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Vydané v:Journal of systems science and complexity Ročník 36; číslo 2; s. 822 - 842
Hlavní autori: Xu, Xiaoli, Zhou, Yan, Zhang, Kongsheng, Zhao, Mingtao
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Berlin/Heidelberg Springer Berlin Heidelberg 01.04.2023
Springer Nature B.V
Predmet:
Coefficients
Complex Systems
Control
Data analysis
Estimators
Feature selection
Mathematical functions
Mathematical models
Mathematics
Mathematics and Statistics
Mathematics of Computing
Operations Research/Decision Theory
Separation
Simulation
Social sciences
Statistics
Systems Theory
Variables
varying coefficient models
Double-penalized quadratic inference functions
variable selection
longitudinal data
ISSN:1009-6124, 1559-7067
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Popis
Shrnutí:Variable selection for varying coefficient models includes the separation of varying and constant effects, and the selection of variables with nonzero varying effects and those with nonzero constant effects. This paper proposes a unified variable selection approach called the double-penalized quadratic inference functions method for varying coefficient models of longitudinal data. The proposed method can not only separate varying coefficients and constant coefficients, but also estimate and select the nonzero varying coefficients and nonzero constant coefficients. It is suitable for variable selection of linear models, varying coefficient models, and partial linear varying coefficient models. Under regularity conditions, the proposed method is consistent in both separation and selection of varying coefficients and constant coefficients. The obtained estimators of varying coefficients possess the optimal convergence rate of non-parametric function estimation, and the estimators of nonzero constant coefficients are consistent and asymptotically normal. Finally, the authors investigate the finite sample performance of the proposed method through simulation studies and a real data analysis. The results show that the proposed method performs better than the existing competitor.
Bibliografia:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:1009-6124
1559-7067
DOI:10.1007/s11424-022-2109-1