Varying-coefficient single-index model for longitudinal data

被引：0
|
作者：
Lin, Hongmei [1 ]
Zhang, Riquan [1 ]
Shi, Jianhong [2 ]
Wang, Yuedong [3 ]
机构：
[1] East China Normal Univ, Sch Stat, Shanghai 200241, Peoples R China
[2] Shanxi Normal Univ, Sch Math & Comp Sci, Linfen 041004, Peoples R China
[3] Univ Calif Santa Barbara, Dept Stat & Appl Probabil, Santa Barbara, CA 93106 USA
来源：
STATISTICS AND ITS INTERFACE | 2017年 / 10卷 / 03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：
Varying-coefficient single-index models; Cholesky decomposition; Local linear regression; Longitudinal data; EFFICIENT ESTIMATION; SEMIPARAMETRIC ESTIMATION; COVARIANCE-STRUCTURES; INFERENCES; ROBUST;
D O I：
10.4310/SII.2017.v10.n3.a12
中图分类号：
Q [生物科学];
学科分类号：
07 ; 0710 ; 09 ;
摘要：
In this paper we consider a general class of varying coefficient single-index models for longitudinal data. This class of models provides a tool for simultaneous dimension reduction and the exploration of dynamic patterns. We develop an estimation procedure using Cholesky decomposition, local linear and backfitting technique. Asymptotic normality for the proposed estimators of varying-coefficient functions, link function and parameters will be established. Monte Carlo simulation studies show excellent finite-sample performance. We illustrate our methods with a real data example.
引用
收藏
页码：495 / 504
页数：10
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