Bias-corrected GEE estimation and smooth-threshold GEE variable selection for single-index models with clustered data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:105

Article

Lai, Peng^1,2  Wang, Qihua^3,4  Lian, Heng¹ 

[1] Nanyang Technol Univ, Sch Phys & Math Sci, Div Math Sci, Singapore 637371, Singapore

[2] Nanjing Univ Informat Sci & Technol, Coll Math & Phys, Nanjing 210044, Jiangsu, Peoples R China

[3] Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China

[4] Yunnan Univ, Sch Math & Stat, Kunming 650091, Peoples R China

关键词: Generalized estimating equation;    Longitudinal data;    Oracle property;    Single-index model;    Variable selection;   

DOI  :  10.1016/j.jmva.2011.08.009

来源: Elsevier

【 摘 要 】

In this paper, we present an estimation approach based on generalized estimating equations and a variable selection procedure for single-index models when the observed data are clustered. Unlike the case of independent observations, bias-correction is necessary when general working correlation matrices are used in the estimating equations. Our variable selection procedure based on smooth-threshold estimating equations (Ueki (2009) [23]) can automatically eliminate irrelevant parameters by setting them as zeros and is computationally simpler than alternative approaches based on shrinkage penalty. The resulting estimator consistently identifies the significant variables in the index, even when the working correlation matrix is misspecified. The asymptotic property of the estimator is the same whether or not the nonzero parameters are known (in both cases we use the same estimating equations), thus achieving the oracle property in the sense of Fan and Li (2001) [10]. The finite sample properties of the estimator are illustrated by some simulation examples, as well as a real data application. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

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