数学物理学报(英文版)

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ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA

秦国友; 朱仲义   

  1. 华东师范大学统计系, 上海 200062 复旦大学公共卫生学院, 上海 200062
  • 收稿日期:2006-01-12 修回日期:2006-06-05 出版日期:2008-04-20 发布日期:2008-04-20
  • 通讯作者: 朱仲义
  • 基金资助:

    This work was partly supported by the Natural Science Foundation of China (10371042, 10671038)

ROBUST ESTIMATION IN PARTIAL LINEAR MIXED MODEL FOR LONGITUDINAL DATA

Qin Guoyou; Zhu Zhongyi   

  1. Department of Statistics, East China Normal University, Shanghai 200062, China
    Department of Biostatistics, School of Public Health, Fudan University, Shanghai 200032, China
  • Received:2006-01-12 Revised:2006-06-05 Online:2008-04-20 Published:2008-04-20
  • Contact: Zhu Zhongyi

摘要:

In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo
Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.

关键词: Generalized estimating equation, longitudinal data, metropolis
algorithm,
mixed effect, partial linear model, robustness

Abstract:

In this article, robust generalized estimating equation for the analysis of partial linear mixed model for longitudinal data is used. The authors approximate the nonparametric function by a regression spline. Under some regular conditions, the asymptotic properties of the estimators are obtained. To avoid the computation of high-dimensional integral, a robust Monte Carlo
Newton-Raphson algorithm is used. Some simulations are carried out to study the performance of the proposed robust estimators. In addition, the authors also study the robustness and the efficiency of the proposed estimators by simulation. Finally, two real longitudinal data sets are analyzed.

Key words: Generalized estimating equation, longitudinal data, metropolis
algorithm,
mixed effect, partial linear model, robustness

中图分类号: 

  • 62F35