Model detection and variable selection for varying coefficient models with longitudinal data

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• 作者：San Ying Feng ; Yu Ping Hu ; Liu Gen Xue
• 关键词：Combined penalization ; longitudinal data ; model detection ; variable selection ; oracle property ; varying coefficient model ; 62G05 ; 62G20
• 刊名：Acta Mathematica Sinica
• 出版年：2016
• 出版时间：March 2016
• 年：2016
• 卷：32
• 期：3
• 页码：331-350
• 全文大小：356 KB
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• 作者单位：San Ying Feng (1) (2)
  Yu Ping Hu (1) (2)
  Liu Gen Xue (3)
  
  1. School of Mathematics and Statistics, Zhengzhou University, Zhengzhou, 450001, P. R. China
  2. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, P. R. China
  3. College of Applied Sciences, Beijing University of Technology, Beijing, 100124, P. R. China
  
• 刊物类别：Mathematics and Statistics
• 刊物主题：Mathematics
  Mathematics
  Chinese Library of Science
  
• 出版者：Institute of Mathematics, Chinese Academy of Sciences and Chinese Mathematical Society, co-published
• ISSN：1439-7617

文摘

In this paper, we consider the problem of variable selection and model detection in varying coefficient models with longitudinal 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. Keywords Combined penalization longitudinal data model detection variable selection oracle property varying coefficient model