Zero-Inflated Negative Binomial Regression Model with Right Censoring Count Data

Abstract

A Poisson model is typically assumed for count data, but when there are so many zeros in the response variable, because of overdispersion, a negative binomial regression is suggested as a count regression instead of Poisson regression. In this paper, a zero-inflated negative binomial regression model with right censoring count data is developed. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness-of-fit for the regression model is examined. We study the effects of censoring in terms of parameters estimation and their standard errors via simulation.