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Nonparametric inference based on panel count data

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Abstract

Panel count data usually refer to data arising from studies on recurrent events in which the subjects under study are followed or observed only periodically rather than continuously. In such situations, an objective of interest is about the occurrence of some events that can occur multiple times or repeatedly and the studies resulting in this type of information are often referred to as event history studies. There are many fields such as medical studies, reliability experiments and social sciences wherein panel count data are encountered commonly. This article reviews basic concepts about panel count data, some common issues and questions of interest regarding them as well as the corresponding statistical procedures that are suitable for their analysis. In particular, we will discuss an estimation of the mean function of the underlying counting process characterizing the occurrence of the events, comparison of several processes and analysis of multiple state panel count data. Some discussion is also presented of situations involving dependent or informative observation processes.

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Authors and Affiliations

  1. Department of Applied Mathematics, The Hong Kong Polytechnic University, Hong Kong, Hong Kong

    Xingqiu Zhao

  2. Department of Mathematics & Statistics, McMaster University, Hamilton, Canada, L8S 4K1

    N. Balakrishnan

  3. Department of Statistics, University of Missouri, Columbia, MO, 65211, USA

    Jianguo Sun

Authors
  1. Xingqiu Zhao
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  2. N. Balakrishnan
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  3. Jianguo Sun
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Corresponding author

Correspondence to Jianguo Sun.

Additional information

Communicated by Domingo Morales.

This invited paper is discussed in the comments available at: doi:10.1007/s11749-010-0224-0, doi:10.1007/s11749-010-0225-z, doi:10.1007/s11749-010-0226-y, doi:10.1007/s11749-010-0227-x, doi:10.1007/s11749-010-0228-9, doi:10.1007/s11749-010-0229-8.

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Zhao, X., Balakrishnan, N. & Sun, J. Nonparametric inference based on panel count data. TEST 20, 1–42 (2011). https://doi.org/10.1007/s11749-010-0223-1

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