Hurdle negative binomial regression model with right censored count data

Seyed Ehsan Saffari, Robiah Adnan, William Greene

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20 Citations (Scopus)

Abstract

A Poisson model typically is assumed for count data. In many cases because of many zeros in the response variable, the mean is not equal to the variance value of the dependent variable. Therefore, the Poisson model is no longer suitable for this kind of data. Thus, we suggest using a hurdle negative binomial regression model to overcome the problem of overdispersion. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle negative binomial regression model is introduced on count data with many zeros. The estimation of regression parameters using maximum likelihood is discussed and the goodness-of-fit for the regression model is examined.

Original languageEnglish
Pages (from-to)181-194
Number of pages14
JournalSORT
Volume36
Issue number2
Publication statusPublished - Jul 2012
Externally publishedYes

Keywords

  • Censored data
  • Hurdle negative binomial regression
  • Maximum likelihood method
  • Simulation

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