IDEAS home Printed from https://ideas.repec.org/a/eee/matcom/v69y2005i1p4-11.html
   My bibliography  Save this article

A rich family of generalized Poisson regression models with applications

Author

Listed:
  • Bae, S.
  • Famoye, F.
  • Wulu, J.T.
  • Bartolucci, A.A.
  • Singh, K.P.

Abstract

The Poisson regression (PR) model is inappropriate for modeling over- or under-dispersed (or inflated) data. Several generalizations of PR model have been proposed for modeling such data. In this paper, a rich family of generalized Poisson regression (GPR) models is reviewed in detail. The family has a wide range of applications in various disciplines including agriculture, econometrics, patent applications, species abundance, medicine, and use of recreational facilities. For illustrating the usefulness of the family, several applications with different situations are given. For example, hospital discharge counts are modeled using GPR and other generalized models, in which the applied models show that household size, education, and income are positively related to diagnosis-related groups (DRGs) hospital discharges. One of the advantages of using the family is that it lets data determine which model is appropriate for a given situation. It is expected that the results discussed in the paper would enhance our understanding of various forms of count data originating from primary health care facilities and medical domains.

Suggested Citation

  • Bae, S. & Famoye, F. & Wulu, J.T. & Bartolucci, A.A. & Singh, K.P., 2005. "A rich family of generalized Poisson regression models with applications," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 4-11.
    • Handle: RePEc:eee:matcom:v:69:y:2005:i:1:p:4-11
    DOI: 10.1016/j.matcom.2005.02.026
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0378475405000637
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.matcom.2005.02.026?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Gurmu, Shiferaw & Trivedi, Pravin K, 1996. "Excess Zeros in Count Models for Recreational Trips," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(4), pages 469-477, October.
    2. Gurmu, Shiferaw, 1997. "Semi-Parametric Estimation of Hurdle Regression Models with an Application to Medicaid Utilization," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 12(3), pages 225-243, May-June.
    3. Mullahy, John, 1986. "Specification and testing of some modified count data models," Journal of Econometrics, Elsevier, vol. 33(3), pages 341-365, December.
    4. D. Böhning & E. Dietz & P. Schlattmann & L. Mendonça & U. Kirchner, 1999. "The zero‐inflated Poisson model and the decayed, missing and filled teeth index in dental epidemiology," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 162(2), pages 195-209.
    5. Gupta, Pushpa L. & Gupta, Ramesh C. & Tripathi, Ram C., 1996. "Analysis of zero-adjusted count data," Computational Statistics & Data Analysis, Elsevier, vol. 23(2), pages 207-218, December.
    6. Daniel B. Hall, 2000. "Zero-Inflated Poisson and Binomial Regression with Random Effects: A Case Study," Biometrics, The International Biometric Society, vol. 56(4), pages 1030-1039, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Miguel Santolino & Jean-Philippe Boucher, 2009. "Modelling the disability severity score in motor insurance claims: an application to the Spanish case," IREA Working Papers 200902, University of Barcelona, Research Institute of Applied Economics, revised Jan 2009.
    2. Feng-Chang Xie & Jin-Guan Lin & Bo-Cheng Wei, 2010. "Testing for varying zero-inflation and dispersion in generalized Poisson regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1509-1522.
    3. Adewale Isaac Olutumise & Taiwo Olarotimi Oladayo & Lawrence Olusola Oparinde & Igbekele Amos Ajibefun & Taye Timothy Amos & Yiseyon Sunday Hosu & Idowu Alimi, 2023. "Determinants of Health Management Practices’ Utilization and Its Effect on Poultry Farmers’ Income in Ondo State, Nigeria," Sustainability, MDPI, vol. 15(3), pages 1-21, January.
    4. Claudia Czado & Holger Schabenberger & Vinzenz Erhardt, 2014. "Non nested model selection for spatial count regression models with application to health insurance," Statistical Papers, Springer, vol. 55(2), pages 455-476, May.
    5. Pan Wu & Jinlong Li & Yuzhuang Pian & Xiaochen Li & Zilin Huang & Lunhui Xu & Guilin Li & Ruonan Li, 2022. "How Determinants Affect Transfer Ridership between Metro and Bus Systems: A Multivariate Generalized Poisson Regression Analysis Method," Sustainability, MDPI, vol. 14(15), pages 1-31, August.
    6. Chen, Yang & Luo, Ziyan & Kong, Lingchen, 2021. "ℓ2,0-norm based selection and estimation for multivariate generalized linear models," Journal of Multivariate Analysis, Elsevier, vol. 185(C).
    7. Min-Hsiao Tsai & Ting Hsiang Lin, 2017. "Modeling data with a truncated and inflated Poisson distribution," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(3), pages 383-401, August.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Boncinelli, Fabio & Bartolini, Fabio & Casini, Leonardo, 2018. "Structural factors of labour allocation for farm diversification activities," Land Use Policy, Elsevier, vol. 71(C), pages 204-212.
    2. Abbas Moghimbeigi & Mohammed Reza Eshraghian & Kazem Mohammad & Brian Mcardle, 2008. "Multilevel zero-inflated negative binomial regression modeling for over-dispersed count data with extra zeros," Journal of Applied Statistics, Taylor & Francis Journals, vol. 35(10), pages 1193-1202.
    3. Stefano Mainardi, 2003. "Testing convergence in life expectancies: count regression models on panel data," Prague Economic Papers, Prague University of Economics and Business, vol. 2003(4), pages 350-370.
    4. Tousifur Rahman & Partha Jyoti Hazarika & M. Masoom Ali & Manash Pratim Barman, 2022. "Three-Inflated Poisson Distribution and its Application in Suicide Cases of India During Covid-19 Pandemic," Annals of Data Science, Springer, vol. 9(5), pages 1103-1127, October.
    5. Cristian Roner & Claudia Di Caterina & Davide Ferrari, 2021. "Exponential Tilting for Zero-inflated Interval Regression with Applications to Cyber Security Survey Data," BEMPS - Bozen Economics & Management Paper Series BEMPS85, Faculty of Economics and Management at the Free University of Bozen.
    6. A. Baccini & L. Barabesi & M. Cioni & C. Pisani, 2014. "Crossing the hurdle: the determinants of individual scientific performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(3), pages 2035-2062, December.
    7. Constantinescu Corina D. & Kozubowski Tomasz J. & Qian Haoyu H., 2019. "Probability of ruin in discrete insurance risk model with dependent Pareto claims," Dependence Modeling, De Gruyter, vol. 7(1), pages 215-233, January.
    8. Michael D. Creel & Montserrat Farell, 2001. "Likelihood-Based Approaches to Modeling Demand for Medical Care," UFAE and IAE Working Papers 498.01, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    9. Michael Creel & Montserrat Farell, 2005. "Modeling Usage of Medical Care Services: The Medical Expenditure Panel Survey Data, 1996-2000," UFAE and IAE Working Papers 646.05, Unitat de Fonaments de l'Anàlisi Econòmica (UAB) and Institut d'Anàlisi Econòmica (CSIC).
    10. Bilgic, Abdulbaki & Florkowski, Wojciech J., 2003. "Application Of Hurdle Negative Binomial Count Data Model To Demand For Black Bass Fishing In The Southeastern United States," 2003 Annual Meeting, February 1-5, 2003, Mobile, Alabama 35079, Southern Agricultural Economics Association.
    11. Tanabe, Ryunosuke & Hamada, Etsuo, 2016. "Objective priors for the zero-modified model," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 92-97.
    12. E. Bahrami Samani & Y. Amirian & M. Ganjali, 2012. "Likelihood estimation for longitudinal zero-inflated power series regression models," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(9), pages 1965-1974, May.
    13. John Haslett & Andrew C. Parnell & John Hinde & Rafael de Andrade Moral, 2022. "Modelling Excess Zeros in Count Data: A New Perspective on Modelling Approaches," International Statistical Review, International Statistical Institute, vol. 90(2), pages 216-236, August.
    14. Yundan Xiao & Xiongqing Zhang & Ping Ji, 2015. "Modeling Forest Fire Occurrences Using Count-Data Mixed Models in Qiannan Autonomous Prefecture of Guizhou Province in China," PLOS ONE, Public Library of Science, vol. 10(3), pages 1-12, March.
    15. Fabio Sigrist & Werner A. Stahel, 2010. "Using The Censored Gamma Distribution for Modeling Fractional Response Variables with an Application to Loss Given Default," Papers 1011.1796, arXiv.org, revised May 2012.
    16. Georgios Papadopoulos, 2013. "Immigration Status and Victimization: Evidence from the British Crime Survey," University of East Anglia Applied and Financial Economics Working Paper Series 042, School of Economics, University of East Anglia, Norwich, UK..
    17. Alberto Baccini & Lucio Barabesi & Martina Cioni & Caterina Pisani, 2013. "Crossing the hurdle: the determinants of individual scientific performance," Department of Economics University of Siena 691, Department of Economics, University of Siena.
    18. Moghimbeigi, Abbas & Eshraghian, Mohammad Reza & Mohammad, Kazem & McArdle, Brian, 2009. "A score test for zero-inflation in multilevel count data," Computational Statistics & Data Analysis, Elsevier, vol. 53(4), pages 1239-1248, February.
    19. Bilgic, Abdulbaki & Florkowski, Wojciech J., 2003. "Truncated-At-Zero Count Data Models With Partial Observability: An Application To The Freshwater Fishing Demand In The Southeastern U.S," 2003 Annual Meeting, February 1-5, 2003, Mobile, Alabama 35185, Southern Agricultural Economics Association.
    20. Yip, Karen C.H. & Yau, Kelvin K.W., 2005. "On modeling claim frequency data in general insurance with extra zeros," Insurance: Mathematics and Economics, Elsevier, vol. 36(2), pages 153-163, April.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:matcom:v:69:y:2005:i:1:p:4-11. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.journals.elsevier.com/mathematics-and-computers-in-simulation/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.