Efficient estimation for marginal generalized partially linear single-index models with longitudinal data

被引：0
作者：
Peirong Xu
Jun Zhang
Xingfang Huang
Tao Wang
机构：
[1] Southeast University,Department of Mathematics
[2] Shenzhen University,Shen Zhen
[3] Yale University,Hong Kong Joint Research Center for Applied Statistical Sciences, Institute of Statistical Sciences, College of Mathematics and Statistics
来源：
TEST | 2016年 / 25卷
关键词：
Generalized partially single-index models; Generalized estimating equations; Longitudinal data; Semiparametric efficiency; 62-02; 62F12; 62G08;
D O I：
暂无
中图分类号：
学科分类号：
摘要：
We consider marginal generalized partially linear single-index models for longitudinal data. A profile generalized estimating equations (GEE)-based approach is proposed to estimate unknown regression parameters. Within a wide range of bandwidths for estimating the nonparametric function, our profile GEE estimator is consistent and asymptotically normal even if the covariance structure is misspecified. Moreover, if the covariance structure is correctly specified, the semiparametric efficiency can be achieved under heteroscedasticity and without distributional assumptions on the covariates. Simulation studies are conducted to evaluate the finite sample performance of the proposed procedure. The proposed methodology is further illustrated through a data analysis.
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页码：413 / 431
页数：18
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