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Regression analysis of mixed panel count data with dependent observation processes

Author

Listed:
  • Lei Ge
  • Jaihee Choi
  • Hui Zhao
  • Yang Li
  • Jianguo Sun

Abstract

Event history data commonly occur in many areas and a great deal of literature on their analysis has been established. However, most of the existing methods apply only to a single type of event history data. Recently, several authors have discussed the analysis of mixed types of event history data and the existence of dependent observation processes is another issue that one often has to deal with in the analysis of event history data. This paper discusses regression analysis of mixed panel count data with dependent observation processes, which has not been addressed in the literature, and for the problem, an approximate likelihood estimation approach is proposed. For the implementation, an EM algorithm is developed and the proposed estimators are shown to be consistent and asymptotically normal. An extensive simulation study is performed to assess the performance of the proposed approach and indicates that it works well in practical situations. An application to a set of real data is provided.

Suggested Citation

  • Lei Ge & Jaihee Choi & Hui Zhao & Yang Li & Jianguo Sun, 2023. "Regression analysis of mixed panel count data with dependent observation processes," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 35(4), pages 669-684, October.
    • Handle: RePEc:taf:gnstxx:v:35:y:2023:i:4:p:669-684
    DOI: 10.1080/10485252.2023.2203275
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