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

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• 作者：Guoyou Qin^a ; ^b ; ^{gyqin@fudan.edu.cn} ; Yang Bai^c ; ^{statbyang@mail.shufe.edu.cn} ; Zhongyi Zhu^d ; ^{zhuzy@fudan.edu.cn}
• 关键词：46N30
• 刊名：Journal of Multivariate Analysis
• 出版年：2012
• 出版时间：February 2012
• 年：2012
• 卷：105
• 期：1
• 页码：32-44
• 全文大小：281 K

文摘

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.