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Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis

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Abstract

In this paper we propose a new approach, called a fuzzy class model for Poisson regression, in the analysis of heterogeneous count data. On the basis of fuzzy set concept and fuzzy classification maximum likelihood (FCML) procedures we create an FCML algorithm for fuzzy class Poisson regression models. Traditionally, the EM algorithm had been used for latent class regression models. Thus, the accuracy and effectiveness of EM and FCML algorithms for estimating the parameters are compared. The results show that the proposed FCML algorithm presents better accuracy and effectiveness and can be used as another good tool to regression analysis for heterogeneous count data.

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Authors and Affiliations

  1. Department of Applied Mathematics, Chung Yuan Christian University, Chung-Li, Taiwan, 32023

    Miin-Shen Yang & Chien-Yo Lai

Authors
  1. Miin-Shen Yang
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  2. Chien-Yo Lai
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Correspondence to Miin-Shen Yang.

Additional information

This work was supported in part by the National Science Council of Taiwan under Grant NSC-89-2213-E-033-007.

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Yang, MS., Lai, CY. Mixture Poisson regression models for heterogeneous count data based on latent and fuzzy class analysis. Soft Comput 9, 519–524 (2005). https://doi.org/10.1007/s00500-004-0369-4

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  • DOI: https://doi.org/10.1007/s00500-004-0369-4

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