Testing the lack-of-fit of zero-inflated Poisson regression models

Chin-Shang Li

Research output: Contribution to journalArticlepeer-review

1 Scopus citations


A zero-inflated Poisson regression model has been widely used for the effect of a covariate in count data containing many zeros with a linear predictor. To assess the adequacy of the linear relationship, we approximate the covariate effect with cubic B-splines. The semiparametric model parameters are estimated by maximizing the likelihood function through an expectation- maximization algorithm. A log-likelihood ratio test is then used to evaluate the adequacy of the linear relation. A simulation study is conducted to study the power performance of the test. A real example is provided to demonstrate the practical use of the methodology.

Original languageEnglish (US)
Pages (from-to)497-510
Number of pages14
JournalCommunications in Statistics: Simulation and Computation
Issue number4
StatePublished - Apr 2011


  • B-splines
  • Expectation-maximization (EM) algorithm
  • Lack-of-fit test
  • Log-likelihood ratio test

ASJC Scopus subject areas

  • Modeling and Simulation
  • Statistics and Probability


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