A test for lack-of-fit of zero-inflated negative binomial models

Chin-Shang Li, Shen Ming Lee, Ming Shan Yeh

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

When a count data set has excessive zero counts, nonzero counts are overdispersed, and the effect of a continuous covariate might be nonlinear, for analysis a semiparametric zero-inflated negative binomial (ZINB) regression model is proposed. The unspecified smooth functional form for the continuous covariate effect is approximated by a cubic spline. The semiparametric ZINB regression model is fitted by maximizing the likelihood function. The likelihood ratio procedure is used to evaluate the adequacy of a postulated parametric functional form for the continuous covariate effect. An extensive simulation study is conducted to assess the finite-sample performance of the proposed test. The practicality of the proposed methodology is demonstrated with data of a motorcycle survey of traffic regulations conducted in 2007 in Taiwan by the Ministry of Transportation and Communication.

Original languageEnglish (US)
JournalJournal of Statistical Computation and Simulation
DOIs
StatePublished - Jan 1 2019
Externally publishedYes

Keywords

  • B-splines
  • count data
  • likelihood ratio
  • overdispersion
  • zero-inflated negative binomial
  • zero-inflated Poisson

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty
  • Applied Mathematics

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