Robust estimation in generalized partial linear models for clustered data

• 成果类型: Article
• 署名作者: He, XM; Fung, WK; Zhu, ZY
• 署名单位: University of Illinois System; University of Illinois Urbana-Champaign; University of Hong Kong; East China Normal University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214505000000277
• 发表日期: 2005
• 页码: 1176-1184
• 关键词: semiparametric regression; longitudinal data; EFFICIENCY; spline

摘要

In this article we consider robust generalized estimating equations for the analysis of semiparametric generalized partial linear models (GPLMs) for longitudinal data or clustered data in general. We approximate the nonparametric function in the GPLM by a regression spline, and use bounded scores and leverage-based weights in the estimating equation to achieve robustness against outliers. We show that the regression spline approach avoids some of the intricacies associated with the profile-kernel method, and that robust estimation and inference can be carried out operationally as if a generalized linear model were used.