Model Detection and Variable Selection for Varying Coefficient Models with Longitudinal Data

In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficie...

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Bibliographic Details
Published inActa mathematica Sinica. English series Vol. 32; no. 3; pp. 331 - 350
Main Authors Feng, San Ying, Hu, Yu Ping, Xue, Liu Gen
Format Journal Article
LanguageEnglish
Published Beijing Institute of Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society 01.03.2016
Springer Nature B.V
Subjects
Coefficients
Constants
Data analysis
Data processing
Estimators
Feature selection
Hypothesis testing
Mathematical analysis
Mathematical models
Mathematics
Mathematics and Statistics
Simulation
Studies
Theorems
Tuning
Variables
函数
数学
数学理论
积分
算术
62G05
model detection
Combined penalization
varying coefficient model
oracle property
62G20
variable selection
longitudinal data
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Summary:In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.
Bibliography:In this puper, we consider the problem of variabie selection and model detection in varying coefficient models with longitudinM data. We propose a combined penalization procedure to select the significant variables, detect the true structure of the model and estimate the unknown regression coefficients simultaneously. With appropriate selection of the tuning parameters, we show that the proposed procedure is consistent in both variable selection and the separation of varying and constant coefficients, and the penalized estimators have the oracle property. Finite sample performances of the proposed method are illustrated by some simulation studies and the real data analysis.
Combined penalization, longitudinal data, model detection, variable selection, oracle property, varying coefficient model
11-2039/O1
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1439-8516
1439-7617
DOI:10.1007/s10114-016-4639-8