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 journalArticle

1 Citation (Scopus)

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

Fingerprint

Negative Binomial Model
Lack of Fit
Covariates
Binomial Model
Negative Binomial
Motorcycles
Regression Model
Count
Zero
Splines
Count Data
Cubic Spline
Likelihood Ratio
Taiwan
Likelihood Function
Communication
Traffic
Simulation Study
Methodology
Evaluate

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

Cite this

A test for lack-of-fit of zero-inflated negative binomial models. / Li, Chin-Shang; Lee, Shen Ming; Yeh, Ming Shan.

In: Journal of Statistical Computation and Simulation, 01.01.2019.

Research output: Contribution to journalArticle

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