A test for lack-of-fit of zero-inflated negative binomial models. Issue 7 (3rd May 2019)

• Record Type: Journal Article
• Authors: Li, Chin-Shang; Lee, Shen-Ming; Yeh, Ming-Shan
• Is Part Of: Journal of statistical computation and simulation. Volume 89:Issue 7(2019)
• Page Start: 1301
• Page End: 1321
• Publication Date: 2019-05-03
• Subjects: B-splines -- count data -- likelihood ratio -- overdispersion -- zero-inflated negative binomial -- zero-inflated Poisson; 62G10; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5028505
• DOI: 10.1080/00949655.2019.1577856
• Languages: English
• ISSNs: 0094-9655

Abstract

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.