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Robust estimation for panel count data with informative observation times and censoring times

  • Hangjin Jiang
  • Wen Su
  • Xingqiu Zhao
Article
  • 30 Downloads

Abstract

We consider the semiparametric regression of panel count data occurring in longitudinal follow-up studies that concern occurrence rate of certain recurrent events. The analysis of panel count data involves two processes, i.e, a recurrent event process of interest and an observation process controlling observation times. However, the model assumptions of existing methods, such as independent censoring time and Poisson assumption, are restrictive and questionable. In this paper, we propose new joint models for panel count data by considering both informative observation times and censoring times. The asymptotic normality of the proposed estimators are established. Numerical results from simulation studies and a real data example show the advantage of the proposed method.

Keywords

Semiparametric regression Panel count data Informative observation times Informative censoring times Robust estimation 

Notes

Acknowledgements

The authors would like to thank the Editor, the Associate Editor and the two reviewers for their constructive and insightful comments and suggestions that greatly improved the paper. This research is partly supported by the Research Grant Council of Hong Kong (15301218), the National Natural Science Foundation of China (No. 11771366), and The Hong Kong Polytechnic University.

Compliance with ethical standards

Conflict of interest

No potential conflict of interest was reported by the authors.

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Center for Data ScienceZheJiang UniversityHangzhouChina
  2. 2.Department of StatisticsThe Chinese University of Hong KongShatinHong Kong
  3. 3.Haitong International Securities GroupKowloonHong Kong
  4. 4.Department of Applied MathematicsThe Hong Kong Polytechnic UniversityHung HomHong Kong

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