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

Published

Journal Article

© 2018 Taylor & Francis Group, LLC. 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.

Full Text

Duke Authors

Cited Authors

  • Fu, Q; Guo, X; Land, KC

Published Date

  • January 17, 2018

Published In

Volume / Issue

  • 47 / 2

Start / End Page

  • 427 - 447

Electronic International Standard Serial Number (EISSN)

  • 1532-415X

International Standard Serial Number (ISSN)

  • 0361-0926

Digital Object Identifier (DOI)

  • 10.1080/03610926.2017.1303736

Citation Source

  • Scopus