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A Poisson-multinomial mixture approach to grouped and right-censored counts

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  • Qiang Fu
  • Xin Guo
  • Kenneth C. Land

Abstract

Although count data are often collected in social, psychological, and epidemiological surveys in grouped and right-censored categories, there is a lack of statistical methods simultaneously taking both grouping and right-censoring into account. In this research, we propose a new generalized Poisson-multinomial mixture approach to model grouped and right-censored (GRC) count data. Based on a mixed Poisson-multinomial process for conceptualizing grouped and right-censored count data, we prove that the new maximum-likelihood estimator (MLE-GRC) is consistent and asymptotically normally distributed for both Poisson and zero-inflated Poisson models. The use of the MLE-GRC, implemented in an R function, is illustrated by both statistical simulation and empirical examples. This research provides a tool for epidemiologists to estimate incidence from grouped and right-censored count data and lays a foundation for regression analyses of such data structure.

Suggested Citation

  • Qiang Fu & Xin Guo & Kenneth C. Land, 2018. "A Poisson-multinomial mixture approach to grouped and right-censored counts," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(2), pages 427-447, January.
    • Handle: RePEc:taf:lstaxx:v:47:y:2018:i:2:p:427-447
    DOI: 10.1080/03610926.2017.1303736
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    Cited by:

    1. Qiang Fu & Tian‐Yi Zhou & Xin Guo, 2021. "Modified Poisson regression analysis of grouped and right‐censored counts," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 184(4), pages 1347-1367, October.

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