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Generalized Count Data Regression Models and Their Applications to Health Care Data

Author

Listed:
  • Carl Lee

    (Central Michigan University)

  • Felix Famoye

    (Central Michigan University)

  • Alfred Akinsete

    (Marshall University)

Abstract

A method for developing generalized parametric regression models for count data is proposed and studied. The method is based on the framework of the T-geometric family of distributions. A T-geometric family consists of discrete distributions, which are analogues to the continuous distributions for the random variable T. The general methodology is applied to derive some generalized regression models for count data. These regression models can fit count data that are under-dispersed, equi-dispersed or over-dispersed. The extension to model truncated or inflated data is addressed. Some new generalized T-geometric regression models are applied to real world data sets to illustrate the flexibility of the models. The models were fitted to four response variables from health care data and their performance compared. No single regression model outperforms other models for all the four response variables. Thus, a researcher should evaluate different models before selecting a final regression model for a count response variable.

Suggested Citation

  • Carl Lee & Felix Famoye & Alfred Akinsete, 2021. "Generalized Count Data Regression Models and Their Applications to Health Care Data," Annals of Data Science, Springer, vol. 8(2), pages 367-386, June.
    • Handle: RePEc:spr:aodasc:v:8:y:2021:i:2:d:10.1007_s40745-019-00221-8
    DOI: 10.1007/s40745-019-00221-8
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    References listed on IDEAS

    as
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    3. Mullahy, John, 1997. "Heterogeneity, Excess Zeros, and the Structure of Count Data Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 337-350, May-June.
    4. Rainer Winkelmann, 2008. "Econometric Analysis of Count Data," Springer Books, Springer, edition 0, number 978-3-540-78389-3, September.
    5. Peter Congdon, 2017. "Quantile regression for overdispersed count data: a hierarchical method," Journal of Statistical Distributions and Applications, Springer, vol. 4(1), pages 1-19, December.
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    7. Felix Famoye & Carl Lee, 2017. "Exponentiated-exponential geometric regression model," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(16), pages 2963-2977, December.
    8. Cameron, A Colin & Johansson, Per, 1997. "Count Data Regression Using Series Expansions: With Applications," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 203-223, May-June.
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