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A bivariate Poisson count data model using conditional probabilities

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  • Peter Berkhout
  • Erik Plug

Abstract

The applied econometrics of bivariate count data predominantly focus on a bivariate Poisson density with a correlation structure that is very restrictive. The main limitation is that this bivariate distribution excludes zero and negative correlation. This paper introduces a new model which allows for a more flexible correlation structure. To this end the joint density is decomposed by means of the multiplication rule in marginal and conditional densities. Simulation experiments and an application of the model to recreational data are presented.

Suggested Citation

  • Peter Berkhout & Erik Plug, 2004. "A bivariate Poisson count data model using conditional probabilities," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 58(3), pages 349-364, August.
    • Handle: RePEc:bla:stanee:v:58:y:2004:i:3:p:349-364
    DOI: 10.1111/j.1467-9574.2004.00126.x
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    Cited by:

    1. Najla Qarmalah & Abdulhamid A. Alzaid, 2023. "Zero-Dependent Bivariate Poisson Distribution with Applications," Mathematics, MDPI, vol. 11(5), pages 1-16, February.
    2. Rufin Bidounga & Evrand Giles Brunel Mandangui Maloumbi & Réolie Foxie Mizélé Kitoti & Dominique Mizère, 2020. "The New Bivariate Conway-Maxwell-Poisson Distribution Obtained by the Crossing Method," International Journal of Statistics and Probability, Canadian Center of Science and Education, vol. 9(6), pages 1-1, November.
    3. Aristidis Nikoloulopoulos & Dimitris Karlis, 2010. "Regression in a copula model for bivariate count data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1555-1568.
    4. F. Novoa-Muñoz & M. Jiménez-Gamero, 2014. "Testing for the bivariate Poisson distribution," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 77(6), pages 771-793, August.
    5. Kokonendji, Célestin C. & Puig, Pedro, 2018. "Fisher dispersion index for multivariate count distributions: A review and a new proposal," Journal of Multivariate Analysis, Elsevier, vol. 165(C), pages 180-193.
    6. Jacek Osiewalski & Jerzy Marzec, 2019. "Joint modelling of two count variables when one of them can be degenerate," Computational Statistics, Springer, vol. 34(1), pages 153-171, March.
    7. Sobom M. Somé & Célestin C. Kokonendji & Nawel Belaid & Smail Adjabi & Rahma Abid, 2023. "Bayesian local bandwidths in a flexible semiparametric kernel estimation for multivariate count data with diagnostics," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(3), pages 843-865, September.
    8. Lundevaller, Erling Häggström, 2009. "The effect of travel cost on frequencies of shopping and recreational trips in Sweden," Journal of Transport Geography, Elsevier, vol. 17(3), pages 208-215.
    9. Xiaoping Jin & Bradley P. Carlin & Sudipto Banerjee, 2005. "Generalized Hierarchical Multivariate CAR Models for Areal Data," Biometrics, The International Biometric Society, vol. 61(4), pages 950-961, December.
    10. Kennedy Ndue & Melese Mulu Baylie & Pál Goda, 2023. "Determinants of Rural Households’ Intensity of Flood Adaptation in the Fogera Rice Plain, Ethiopia: Evidence from Generalised Poisson Regression," Sustainability, MDPI, vol. 15(14), pages 1-19, July.
    11. Luiza S. C. Piancastelli & Wagner Barreto‐Souza & Hernando Ombao, 2023. "Flexible bivariate INGARCH process with a broad range of contemporaneous correlation," Journal of Time Series Analysis, Wiley Blackwell, vol. 44(2), pages 206-222, March.
    12. Rajib Dey & M. Ataharul Islam, 2017. "A conditional count model for repeated count data and its application to GEE approach," Statistical Papers, Springer, vol. 58(2), pages 485-504, June.
    13. Shanmugam R, 2018. "Impact of Raising Copayment and/or Reducing Reimbursement Benefits on Healthcare according to “Disequilibrium Bivariate Distributionâ€," Biostatistics and Biometrics Open Access Journal, Juniper Publishers Inc., vol. 5(1), pages 1-13, February.
    14. Rolf Larsson, 2020. "Discrete factor analysis using a dependent Poisson model," Computational Statistics, Springer, vol. 35(3), pages 1133-1152, September.
    15. 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.
    16. Bermúdez, Lluís & Karlis, Dimitris, 2011. "Bayesian multivariate Poisson models for insurance ratemaking," Insurance: Mathematics and Economics, Elsevier, vol. 48(2), pages 226-236, March.

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