A Score Test for Testing a Marginalized Zero-Inflated Poisson Regression Model Against a Marginalized Zero-Inflated Negative Binomial Regression Model

  • Gul Inan
  • John Preisser
  • Kalyan Das


Marginalized zero-inflated count regression models (Long et al. in Stat Med 33(29):5151–5165, 2014) provide direct inference on overall exposure effects. Unlike standard zero-inflated models, marginalized models specify a regression model component for the marginal mean in addition to a component for the probability of an excess zero. This study proposes a score test for testing a marginalized zero-inflated Poisson model against a marginalized zero-inflated negative binomial model for model selection based on an assessment of over-dispersion. The sampling distribution and empirical power of the proposed score test are investigated via a Monte Carlo simulation study, and the procedure is illustrated with data from a horticultural experiment. Supplementary materials accompanying this paper appear on-line.


Count data Excess zeros Marginal models Over-dispersion Score test 



Part of this study was carried out, while Gul Inan was visiting Department of Biostatistics, University of North Carolina—Chapel Hill, USA. She would like to thank the Scientific and Technological Research Council of Turkey (TUBITAK) for funding her postdoctoral studies in USA. This study was supported by the International Biometric Society and Institute of Mathematical Statistics travel grant programmes to be presented at 9th Conference of the Eastern Mediterranean Region of the International Biometric Society and Joint Statistical Meetings, respectively, in 2017.

Supplementary material

13253_2017_314_MOESM2_ESM.pdf (195 kb)
Supplementary material 1 (pdf 194 KB)


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Copyright information

© International Biometric Society 2017

Authors and Affiliations

  1. 1.Department of StatisticsMiddle East Technical UniversityAnkaraTurkey
  2. 2.Department of BiostatisticsUniversity of North CarolinaChapel HillUSA
  3. 3.Department of StatisticsUniversity of CalcuttaKolkataIndia

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