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Appropriate statistical model for zero-inflated count data: simulation based study

Author

Listed:
  • Ayan CHOWDHURY

    (Department of Statistics, University of Chittagong, Bangladesh)

  • Soma Chowdhury BISWAS

    (Department of Statistics, University of Chittagong, Bangladesh)

Abstract

Zero-inflated count data is easily reached in real field. Over-dispersion is the consequence of zero inflation in count data. For modeling this kind of count data, several zero adjusted models such as Zero Inflated Poisson, Zero Inflated Negative Binomial, Hurdle Poisson and Hurdle Negative Binomial models are more suitable than basic statistical models. The best zero adjusted model selection is the key aim of this research. In this study, R code has been used to simulate datasets as well as to compare these models based on Akaike information criterion, Bayesian information criterion and Vuong test. The result of this study suggests that Hurdle Negative Binomial model has been preferred as the best fitted model for count data with excess of zero.

Suggested Citation

  • Ayan CHOWDHURY & Soma Chowdhury BISWAS, 2018. "Appropriate statistical model for zero-inflated count data: simulation based study," Computational Methods in Social Sciences (CMSS), "Nicolae Titulescu" University of Bucharest, Faculty of Economic Sciences, vol. 6(1), pages 5-12, June.
    • Handle: RePEc:ntu:ntcmss:vol6-iss1-5-12
    as

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    References listed on IDEAS

    as
    1. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    2. Zeileis, Achim & Kleiber, Christian & Jackman, Simon, 2008. "Regression Models for Count Data in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 27(i08).
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
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