A simulation study for count data models under varying degrees of outliers and zeros

Tuzen F., Erbas S., OLMUŞ H.

COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, vol.49, no.4, pp.1078-1088, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 49 Issue: 4
  • Publication Date: 2020
  • Doi Number: 10.1080/03610918.2018.1498886
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Business Source Elite, Business Source Premier, CAB Abstracts, Compendex, Computer & Applied Sciences, Veterinary Science Database, zbMATH, Civil Engineering Abstracts
  • Page Numbers: pp.1078-1088
  • Keywords: Count data, Hurdle models, Outliers, Zeroinflated models, POISSON REGRESSION, HURDLE MODELS
  • Gazi University Affiliated: Yes


This study was aimed at examining the performance of count data models under various outliers and zero inflation situations with simulated data. Poisson, Negative Binomial, Zero-inflated Poisson, Zero-inflated Negative Binomial, Poisson Hurdle and Negative Binomial Hurdle models were considered to test how well each of the model fits the selected datasets having outliers and excess zeros. We found that Zero-inflated Negative Binomial and Negative Binomial Hurdle models were found to be more successful than other count data models. Also the results indicated that in some scenarios, the Negative Binomial model outperformed other models in the presence of outliers and/or excess zeros.