Generalized fiducial inference on the mean of zero-inflated Poisson and Poisson hurdle models

Yixuan Zou, Jan Hannig, Derek S. Young

Research output: Contribution to journalArticlepeer-review

6 Scopus citations


Zero-inflated and hurdle models are widely applied to count data possessing excess zeros, where they can simultaneously model the process from how the zeros were generated and potentially help mitigate the effects of overdispersion relative to the assumed count distribution. Which model to use depends on how the zeros are generated: zero-inflated models add an additional probability mass on zero, while hurdle models are two-part models comprised of a degenerate distribution for the zeros and a zero-truncated distribution. Developing confidence intervals for such models is challenging since no closed-form function is available to calculate the mean. In this study, generalized fiducial inference is used to construct confidence intervals for the means of zero-inflated Poisson and Poisson hurdle models. The proposed methods are assessed by an intensive simulation study. An illustrative example demonstrates the inference methods.

Original languageEnglish
Article number5
JournalJournal of Statistical Distributions and Applications
Issue number1
StatePublished - Dec 2021

Bibliographical note

Publisher Copyright:
© 2021, The Author(s).


  • Count data
  • Coverage probability
  • Data dispersion
  • Generalized confidence intervals
  • Zero-truncated poisson

ASJC Scopus subject areas

  • Statistics and Probability
  • Computer Science Applications
  • Statistics, Probability and Uncertainty


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