The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Analysis of Food Poisoning via Zero Inflation Models

  • Jung, Hwan-Sik (Department of Statistics, Seoul National University) ;
  • Kim, Byung-Jip (Department of Statistics, Seoul National University) ;
  • Cho, Sin-Sup (Department of Statistics, Seoul National University) ;
  • Yeo, In-Kwon (Department of Statistics, Sookmyung Women's University)
  • Received : 2012.05.30
  • Accepted : 2012.09.18
  • Published : 2012.10.31
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Abstract

Poisson regression and negative binomial regression are usually used to analyze counting data; however, these models are unsuitable for fit zero-inflated data that contain unexpected zero-valued observations. In this paper, we review the zero-inflated regression in which Bernoulli process and the counting process are hierarchically mixed. It is known that zero-inflated regression can efficiently model the over-dispersion problem. Vuong statistic is employed to compare performances of the zero-inflated models with other standard models.

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References

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Cited by

  1. Prediction of the Number of Food Poisoning Occurrences by Microbes vol.26, pp.6, 2013, https://doi.org/10.5351/KJAS.2013.26.6.923
  2. Models for forecasting food poisoning occurrences vol.23, pp.6, 2012, https://doi.org/10.7465/jkdi.2012.23.6.1117