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Bivariate generalized Poisson regression model: applications on health care data

Author

Listed:
  • Hossein Zamani

    (Hormozgan University)

  • Pouya Faroughi

    (Islamic Azad University)

  • Noriszura Ismail

    (Universiti Kebangsaan Malaysia (UKM))

Abstract

This paper introduces several forms of bivariate generalized Poisson regression model (BGPR) which can be fitted to bivariate and correlated count data with covariates. The main advantage of these forms of BGPR is that they are nested and thus they allow likelihood ratio tests to be performed to choose the best model. The BGPR can be fitted not only to bivariate count data with positive, zero, or negative correlations, but also to under- or overdispersed bivariate count data with flexible form of mean–variance relationship. Applications of several forms of the BGPR are illustrated on two sets of count data: the Australian health survey data and the US National Medical Expenditure Survey data.

Suggested Citation

  • Hossein Zamani & Pouya Faroughi & Noriszura Ismail, 2016. "Bivariate generalized Poisson regression model: applications on health care data," Empirical Economics, Springer, vol. 51(4), pages 1607-1621, December.
    • Handle: RePEc:spr:empeco:v:51:y:2016:i:4:d:10.1007_s00181-015-1051-7
    DOI: 10.1007/s00181-015-1051-7
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    References listed on IDEAS

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    1. Deb, Partha & Trivedi, Pravin K, 1997. "Demand for Medical Care by the Elderly: A Finite Mixture Approach," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 313-336, May-June.
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    4. Felix Famoye & P. Consul, 1995. "Bivariate generalized Poisson distribution with some applications," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 42(1), pages 127-138, December.
    5. 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.
    6. Weiren Wang & Felix Famoye, 1997. "Modeling household fertility decisions with generalized Poisson regression," Journal of Population Economics, Springer;European Society for Population Economics, vol. 10(3), pages 273-283.
    7. Hossein Zamani & Noriszura Ismail, 2013. "Score test for testing zero-inflated Poisson regression against zero-inflated generalized Poisson alternatives," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(9), pages 2056-2068, September.
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    Cited by:

    1. Lluís Bermúdez & Dimitris Karlis, 2022. "Copula-based bivariate finite mixture regression models with an application for insurance claim count data," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(4), pages 1082-1099, December.
    2. Najla Qarmalah & Abdulhamid A. Alzaid, 2023. "Zero-Dependent Bivariate Poisson Distribution with Applications," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    3. Purhadi & Sutikno & Sarni Maniar Berliana & Dewi Indra Setiawan, 2021. "Geographically weighted bivariate generalized Poisson regression: application to infant and maternal mortality data," Letters in Spatial and Resource Sciences, Springer, vol. 14(1), pages 79-99, April.
    4. Carallo, Giulia & Casarin, Roberto & Robert, Christian P., 2024. "Generalized Poisson difference autoregressive processes," International Journal of Forecasting, Elsevier, vol. 40(4), pages 1359-1390.
    5. Sarni Maniar Berliana & Purhadi & Sutikno & Santi Puteri Rahayu, 2020. "Parameter Estimation and Hypothesis Testing of Geographically Weighted Multivariate Generalized Poisson Regression," Mathematics, MDPI, vol. 8(9), pages 1-14, September.

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    More about this item

    Keywords

    Generalized Poisson; Bivariate; Correlation; Overdispersion; Underdispersion;
    All these keywords.

    JEL classification:

    • C35 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Discrete Regression and Qualitative Choice Models; Discrete Regressors; Proportions

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