Modified Poisson regression analysis of grouped and right-censored counts

Qiang Fu, Tian Yi Zhou, Xin Guo

Research output: Journal article publicationJournal articleAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Grouped and right-censored (GRC) counts are widely used in criminology, demography, epidemiology, marketing, sociology, psychology and other related disciplines to study behavioural and event frequencies, especially when sensitive research topics or individuals with possibly lower cognitive capacities are at stake. Yet, the co-existence of grouping and right-censoring poses major difficulties in regression analysis. To implement generalised linear regression of GRC counts, we derive modified Poisson estimators and their asymptotic properties, develop a hybrid line search algorithm for parameter inference, demonstrate the finite-sample performance of these estimators via simulation, and evaluate its empirical applicability based on survey data of drug use in America. This method has a clear methodological advantage over the ordered logistic model for analysing GRC counts.

Original languageEnglish
Pages (from-to)1-21
Number of pages21
JournalJournal of the Royal Statistical Society. Series A: Statistics in Society
DOIs
Publication statusPublished - 8 Apr 2021

Keywords

  • fisher information
  • grouped and right-censored counts
  • hybrid line search
  • modified Poisson estimators
  • regression analysis
  • zero inflation

ASJC Scopus subject areas

  • Statistics and Probability
  • Social Sciences (miscellaneous)
  • Economics and Econometrics
  • Statistics, Probability and Uncertainty

Cite this