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响应变量随机缺失下变系数模型的变量选择
引用本文:赵培信,薛留根.响应变量随机缺失下变系数模型的变量选择[J].数学研究及应用,2011,31(2):251-260.
作者姓名:赵培信  薛留根
作者单位:北京工业大学应用数理学院, 北京 100124; 河池学院数学系, 广西 宜州 546300;北京工业大学应用数理学院, 北京 100124
基金项目:国家自然科学基金(Grant No.10871013),北京市自然科学基金 (Grant No.1072004),广西自然科学基金(Grant No.2010GXNSFB013051),%河池学院研究生科研启动基金(Grant No.2008QS-N014).
摘    要:In this paper,we present a variable selection procedure by combining basis function approximations with penalized estimating equations for varying-coefficient models with missing response at random.With appropriate selection of the tuning parameters,we establish the consistency of the variable selection procedure and the optimal convergence rate of the regularized estimators.A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.

关 键 词:varying-coefficient  model  variable  selection  missing  data
收稿时间:2009/4/15 0:00:00
修稿时间:2009/10/14 0:00:00

Variable Selection for Varying-Coefficient Models with Missing Response at Random
Pei Xin ZHAO and Liu Gen XUE.Variable Selection for Varying-Coefficient Models with Missing Response at Random[J].Journal of Mathematical Research with Applications,2011,31(2):251-260.
Authors:Pei Xin ZHAO and Liu Gen XUE
Institution:1. College of Applied Sciences, Beijing University of Technology, Beijing 100124, P. R. China;Department of Mathematics, Hechi University, Guangxi 546300, P. R. China
2. College of Applied Sciences, Beijing University of Technology, Beijing 100124, P. R. China
Abstract:In this paper, we present a variable selection procedure by combining basis function approximations with penalized estimating equations for varying-coefficient models with missing response at random. With appropriate selection of the tuning parameters, we establish the consistency of the variable selection procedure and the optimal convergence rate of the regularized estimators. A simulation study is undertaken to assess the finite sample performance of the proposed variable selection procedure.
Keywords:varying-coefficient model  variable selection  missing data  
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