Bootstrap tests for overdispersion in a zero-inflated poisson regression model

Byoung Cheol Jung, Myoungshic Jhun, Jae Won Lee

Research output: Contribution to journalReview articlepeer-review

18 Scopus citations


Ridout, Hinde, and Demétrio (2001, Biometrics 57, 219-223) derived a score test for testing a zero-inflated Poisson (ZIP) regression model against zero-inflated negative binomial (ZINB) alternatives. They mentioned that the score test using the normal approximation might underestimate the nominal significance level possibly for small sample cases. To remedy this problem, a parametric hootstrap method is proposed. It is shown that the bootstrap method keeps the significance level close to the nominal one and has greater power uniformly than the existing normal approximation for testing the hypothesis.

Original languageEnglish
Pages (from-to)626-628
Number of pages3
Issue number2
StatePublished - Jun 2005


  • Bootstrap
  • Count data
  • Negative binomial
  • Poisson regression model
  • Score test
  • Zero inflation


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