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

被引：0

作者：

田萍 ^{[1
]}

杨林 ^{[1
]}

薛留根 ^{[2
]}

机构：

[1] Department of Mathematics, Xuchang University

[2] College of Applied Sciences, Beijing University of Technology

来源：

Acta Mathematica Scientia | 2010年 / 30卷 / 03期

基金：

中国国家自然科学基金;

关键词：

Longitudinal data; partially linear single-index model; penalized spline; strong consistency; asymptotic normality;

D O I：

暂无

中图分类号：

O211.67 [期望与预测];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：677 / 687

页数：11

共 9 条

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