Robust estimation of generalized partially linear model for longitudinal data with dropouts

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• 作者：Guoyou Qin ; Zhongyi Zhu ; Wing K. Fung
• 刊名：Annals of the Institute of Statistical Mathematics
• 出版年：2016
• 出版时间：October 2016
• 年：2016
• 卷：68
• 期：5
• 页码：977-1000
• 全文大小：524 KB
• 刊物类别：Mathematics and Statistics
• 刊物主题：Statistics
  Statistics
  Statistics for Business, Economics, Mathematical Finance and Insurance
  
• 出版者：Springer Netherlands
• ISSN：1572-9052
• 卷排序：68

文摘

In this paper, we study the robust estimation of generalized partially linear models (GPLMs) for longitudinal data with dropouts. We aim at achieving robustness against outliers. To this end, a weighted likelihood method is first proposed to obtain the robust estimation of the parameters involved in the dropout model for describing the missing process. Then, a robust inverse probability-weighted generalized estimating equation is developed to achieve robust estimation of the mean model. To approximate the nonparametric function in the GPLM, a regression spline smoothing method is adopted which can linearize the nonparametric function such that statistical inference can be conducted operationally as if a generalized linear model was used. The asymptotic properties of the proposed estimator are established under some regularity conditions, and simulation studies show the robustness of the proposed estimator. In the end, the proposed method is applied to analyze a real data set.KeywordsDropoutsPartially linear modelsRegression splinesRobustness