数学物理学报(英文版) ›› 2010, Vol. 30 ›› Issue (3): 677-687.doi: 10.1016/S0252-9602(10)60069-0

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ASYMPTOTIC PROPERTIES OF ESTIMATORS IN PARTIALLY LINEAR SINGLE-INDEX MODEL FOR LONGITUDINAL DATA

田萍, 杨林, 薛留根   

  1. Department of Mathematics, Xuchang University, Xuchang 461000, China|College of Applied Sciences, Beijing University of Technology, Beijing 100022, China
  • 收稿日期:2006-05-18 修回日期:2008-08-27 出版日期:2010-05-20 发布日期:2010-05-20
  • 基金资助:

    Supported by the National Natural Science Foundation of China (10571008), the Natural Science Foundation of Henan (092300410149), and the Core Teacher Foundation of Henan (2006141)

ASYMPTOTIC PROPERTIES OF ESTIMATORS IN PARTIALLY LINEAR SINGLE-INDEX MODEL FOR LONGITUDINAL DATA

TIAN Ping, YANG Lin, XUE Liu-Gen   

  1. Department of Mathematics, Xuchang University, Xuchang 461000, China|College of Applied Sciences, Beijing University of Technology, Beijing 100022, China
  • Received:2006-05-18 Revised:2008-08-27 Online:2010-05-20 Published:2010-05-20
  • Supported by:

    Supported by the National Natural Science Foundation of China (10571008),
    the Natural Science Foundation of Henan (092300410149), and the Core Teacher Foundation of Henan (2006141)

摘要:

In this article, a partially linear single-index model for longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under  suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.

关键词: Longitudinal data, partially linear single-index model, penalized spline, strong consistency, asymptotic normality

Abstract:

In this article, a partially linear single-index model for longitudinal data is investigated. The generalized penalized spline least squares estimates of the unknown parameters are suggested. All parameters can be estimated simultaneously by the proposed method while the feature of longitudinal data is considered. The existence, strong consistency and asymptotic normality of the estimators are proved under  suitable conditions. A simulation study is conducted to investigate the finite sample performance of the proposed method. Our approach can also be used to study the pure single-index model for longitudinal data.

Key words: Longitudinal data, partially linear single-index model, penalized spline, strong consistency, asymptotic normality

中图分类号: 

  • 62G05