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

作　　者：田萍 杨林 薛留根 

机构地区：[1]Department of Mathematics, Xuchang University [2]College of Applied Sciences, Beijing University of Technology

出　　处：《Acta Mathematica Scientia》2010年第3期677-687,共11页数学物理学报（B辑英文版）

基　　金：Supported by the National Natural Science Foundation of China (10571008);the Natural Science Foundation of Henan (092300410149);the Core Teacher Foundationof Henan (2006141)

摘　　要：In this article, a partially linear single-index model /or 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.In this article, a partially linear single-index model /or 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 

分 类 号：O211.67[理学—概率论与数理统计]