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Solving unobserved heterogeneity with latent class inflated Poisson regression model

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  • Ting Hsiang Lin
  • Min-Hsiao Tsai

Abstract

Inflated data and over-dispersion are two common problems when modeling count data with traditional Poisson regression models. In this study, we propose a latent class inflated Poisson (LCIP) regression model to solve the unobserved heterogeneity that leads to inflations and over-dispersion. The performance of the model estimation is evaluated through simulation studies. We illustrate the usefulness of introducing a latent class variable by analyzing the Behavioral Risk Factor Surveillance System (BRFSS) data, which contain several excessive values and characterized by over-dispersion. As a result, the new model we proposed displays a better fit than the standard Poisson regression and zero-inflated Poisson regression models for the inflated counts.

Suggested Citation

  • Ting Hsiang Lin & Min-Hsiao Tsai, 2022. "Solving unobserved heterogeneity with latent class inflated Poisson regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 49(11), pages 2953-2963, August.
    • Handle: RePEc:taf:japsta:v:49:y:2022:i:11:p:2953-2963
    DOI: 10.1080/02664763.2021.1929875
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