Communications for Statistical Applications and Methods

The Korean Statistical Society (한국통계학회)
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Modeling clustered count data with discrete weibull regression model

  • Yoo, Hanna (Department of Big Data, Busan University of Foreign Studies)
  • Received : 2021.11.22
  • Accepted : 2022.01.13
  • Published : 2022.07.31
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Abstract

In this study we adapt discrete weibull regression model for clustered count data. Discrete weibull regression model has an attractive feature that it can handle both under and over dispersion data. We analyzed the eighth Korean National Health and Nutrition Examination Survey (KNHANES VIII) from 2019 to assess the factors influencing the 1 month outpatient stay in 17 different regions. We compared the results using clustered discrete Weibull regression model with those of Poisson, negative binomial, generalized Poisson and Conway-maxwell Poisson regression models, which are widely used in count data analyses. The results show that the clustered discrete Weibull regression model using random intercept model gives the best fit. Simulation study is also held to investigate the performance of the clustered discrete weibull model under various dispersion setting and zero inflated probabilities. In this paper it is shown that using a random effect with discrete Weibull regression can flexibly model count data with various dispersion without the risk of making wrong assumptions about the data dispersion.

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Acknowledgement

This work was supported by the research grant of the Busan University of Foreign Studies in 2021.

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