Robust empirical likelihood inference for generalized partial linear models with longitudinal data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:105

Article

Qin, Guoyou^2,3  Bai, Yang¹  Zhu, Zhongyi⁴ 

[1] Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai 200433, Peoples R China

[2] Fudan Univ, Dept Biostat, Shanghai 200032, Peoples R China

[3] Fudan Univ, Minist Educ, Key Lab Publ Hlth Safety, Shanghai 200032, Peoples R China

[4] Fudan Univ, Dept Stat, Shanghai 200433, Peoples R China

关键词: B-spline;    Efficiency;    Empirical likelihood;    Generalized estimating equations;    Generalized partial linear models;    Longitudinal data;    Robustness;   

DOI  :  10.1016/j.jmva.2011.08.003

来源: Elsevier

【 摘 要 】

In this paper, we propose a robust empirical likelihood (REL) inference for the parametric component in a generalized partial linear model (GPLM) with longitudinal data. We make use of bounded scores and leverage-based weights in the auxiliary random vectors to achieve robustness against outliers in both the response and covariates. Simulation studies demonstrate the good performance of our proposed REL method, which is more accurate and efficient than the robust generalized estimating equation (GEE) method (X. He, W.K. Fung, Z.Y. Zhu, Robust estimation in generalized partial linear models for clustered data, Journal of the American Statistical Association 100 (2005) 1176-1184). The proposed robust method is also illustrated by analyzing a real data set. (C) 2011 Elsevier Inc. All rights reserved.

【 授权许可】

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