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

Qiang Fu, Xin Guo, Kenneth C. Land

Research output: Journal article publicationJournal articleAcademic researchpeer-review

5 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)427-447
Number of pages21
JournalCommunications in Statistics - Theory and Methods
Volume47
Issue number2
DOIs
Publication statusPublished - 17 Jan 2018

Keywords

  • Grouped and right-censored count data
  • mixed poisson models
  • MLE-GRC
  • multinomial distribution
  • zero-inflated Poisson distribution

ASJC Scopus subject areas

  • Statistics and Probability

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