Local estimation for varying-coefficient models with longitudinal data

被引：2
|
作者：
Lin, Hongmei [1 ]
Zhang, Riquan [2 ]
Shi, Jianhong [3 ]
机构：
[1] Shanghai Univ Int Business & Econ, Sch Stat & Informat, Shanghai, Peoples R China
[2] East China Normal Univ, Sch Stat, Shanghai 200241, Peoples R China
[3] Shanxi Normal Univ, Sch Math & Comp Sci, Linfen, Peoples R China
来源：
COMMUNICATIONS IN STATISTICS-THEORY AND METHODS | 2017年 / 46卷 / 15期
基金：
中国国家自然科学基金; 国家教育部博士点专项基金资助;
关键词：
Cholesky decomposition; local linear regression; longitudinal data; profile least squares; varying-coefficient models; GENERALIZED LINEAR-MODELS; COVARIANCE-STRUCTURES; SERIAL-CORRELATION; LIKELIHOOD;
D O I：
10.1080/03610926.2016.1154156
中图分类号：
O21 [概率论与数理统计]; C8 [统计学];
学科分类号：
020208 ; 070103 ; 0714 ;
摘要：
Varying-coefficient models are very useful for longitudinal data analysis. In this paper, we focus on varying-coefficient models for longitudinal data. We develop a new estimation procedure using Cholesky decomposition and profile least squares techniques. Asymptotic normality for the proposed estimators of varying-coefficient functions has been established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.
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页码：7511 / 7528
页数：18
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