IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i6p1478-d1100540.html
   My bibliography  Save this article

Modeling Under-Dispersed Count Data by the Generalized Poisson Distribution via Two New MM Algorithms

Author

Listed:
  • Xun-Jian Li

    (Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Guo-Liang Tian

    (Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China
    These authors contributed equally to this work.)

  • Mingqian Zhang

    (Department of Statistics and Data Science, Southern University of Science and Technology, Shenzhen 518055, China)

  • George To Sum Ho

    (Department of Supply Chain and Information Management, The Hang Seng University of Hong Kong, Shatin, N.T., Hong Kong, China)

  • Shuang Li

    (Department of Mathematics, Southern University of Science and Technology, Shenzhen 518055, China)

Abstract

Under-dispersed count data often appear in clinical trials, medical studies, demography, actuarial science, ecology, biology, industry and engineering. Although the generalized Poisson (GP) distribution possesses the twin properties of under- and over-dispersion, in the past 50 years, many authors only treat the GP distribution as an alternative to the negative binomial distribution for modeling over-dispersed count data. To our best knowledge, the issues of calculating maximum likelihood estimates (MLEs) of parameters in GP model without covariates and with covariates for the case of under-dispersion were not solved up to now. In this paper, we first develop a new minimization–maximization (MM) algorithm to calculate the MLEs of parameters in the GP distribution with under-dispersion, and then we develop another new MM algorithm to compute the MLEs of the vector of regression coefficients for the GP mean regression model for the case of under-dispersion. Three hypothesis tests (i.e., the likelihood ratio, Wald and score tests) are provided. Some simulations are conducted. The Bangladesh demographic and health surveys dataset is analyzed to illustrate the proposed methods and comparisons with the existing Conway–Maxwell–Poisson regression model are also presented.

Suggested Citation

  • Xun-Jian Li & Guo-Liang Tian & Mingqian Zhang & George To Sum Ho & Shuang Li, 2023. "Modeling Under-Dispersed Count Data by the Generalized Poisson Distribution via Two New MM Algorithms," Mathematics, MDPI, vol. 11(6), pages 1-24, March.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:6:p:1478-:d:1100540
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/6/1478/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/6/1478/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Cameron,A. Colin & Trivedi,Pravin K., 2013. "Regression Analysis of Count Data," Cambridge Books, Cambridge University Press, number 9781107667273.
    2. Kimberly F. Sellers & Sharad Borle & Galit Shmueli, 2012. "The COM‐Poisson model for count data: a survey of methods and applications," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(2), pages 104-116, March.
    3. Kimberly F. Sellers & Darcy S. Morris, 2017. "Underdispersion models: Models that are “under the radar”," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(24), pages 12075-12086, December.
    4. Seth D. Guikema & Jeremy P. Goffelt, 2008. "A Flexible Count Data Regression Model for Risk Analysis," Risk Analysis, John Wiley & Sons, vol. 28(1), pages 213-223, February.
    5. Galit Shmueli & Thomas P. Minka & Joseph B. Kadane & Sharad Borle & Peter Boatwright, 2005. "A useful distribution for fitting discrete data: revival of the Conway–Maxwell–Poisson distribution," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 54(1), pages 127-142, January.
    6. Angers, Jean-Francois & Biswas, Atanu, 2003. "A Bayesian analysis of zero-inflated generalized Poisson model," Computational Statistics & Data Analysis, Elsevier, vol. 42(1-2), pages 37-46, February.
    Full references (including those not matched with items on IDEAS)

    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. Darcy Steeg Morris & Kimberly F. Sellers, 2022. "A Flexible Mixed Model for Clustered Count Data," Stats, MDPI, vol. 5(1), pages 1-18, January.
    2. Douglas Toledo & Cristiane Akemi Umetsu & Antonio Fernando Monteiro Camargo & Idemauro Antonio Rodrigues Lara, 2022. "Flexible models for non-equidispersed count data: comparative performance of parametric models to deal with underdispersion," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 106(3), pages 473-497, September.
    3. Seng Huat Ong & Shin Zhu Sim & Shuangzhe Liu & Hari M. Srivastava, 2023. "A Family of Finite Mixture Distributions for Modelling Dispersion in Count Data," Stats, MDPI, vol. 6(3), pages 1-14, September.
    4. Hossein Kavand & Marcel Voia, 2018. "Estimation of Health Care Demand and its Implication on Income Effects of Individuals," Springer Proceedings in Business and Economics, in: William H. Greene & Lynda Khalaf & Paul Makdissi & Robin C. Sickles & Michael Veall & Marcel-Cristia (ed.), Productivity and Inequality, pages 275-304, Springer.
    5. Zhou, Can & Jiao, Yan & Browder, Joan, 2019. "K-aggregated transformation of discrete distributions improves modeling count data with excess ones," Ecological Modelling, Elsevier, vol. 407(C), pages 1-1.
    6. Dominique Lord & Srinivas Reddy Geedipally & Seth D. Guikema, 2010. "Extension of the Application of Conway‐Maxwell‐Poisson Models: Analyzing Traffic Crash Data Exhibiting Underdispersion," Risk Analysis, John Wiley & Sons, vol. 30(8), pages 1268-1276, August.
    7. S. Hadi Khazraee & Antonio Jose Sáez‐Castillo & Srinivas Reddy Geedipally & Dominique Lord, 2015. "Application of the Hyper‐Poisson Generalized Linear Model for Analyzing Motor Vehicle Crashes," Risk Analysis, John Wiley & Sons, vol. 35(5), pages 919-930, May.
    8. Kimberly F. Sellers & Tong Li & Yixuan Wu & Narayanaswamy Balakrishnan, 2021. "A Flexible Multivariate Distribution for Correlated Count Data," Stats, MDPI, vol. 4(2), pages 1-19, April.
    9. Royce A. Francis & Srinivas Reddy Geedipally & Seth D. Guikema & Soma Sekhar Dhavala & Dominique Lord & Sarah LaRocca, 2012. "Characterizing the Performance of the Conway‐Maxwell Poisson Generalized Linear Model," Risk Analysis, John Wiley & Sons, vol. 32(1), pages 167-183, January.
    10. Morris, Darcy Steeg & Raim, Andrew M. & Sellers, Kimberly F., 2020. "A Conway–Maxwell-multinomial distribution for flexible modeling of clustered categorical data," Journal of Multivariate Analysis, Elsevier, vol. 179(C).
    11. 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.
    12. Bedbur, S. & Kamps, U., 2023. "Uniformly most powerful unbiased tests for the dispersion parameter of the Conway–Maxwell–Poisson distribution," Statistics & Probability Letters, Elsevier, vol. 196(C).
    13. Wang, Xu & Zhang, Xiaobo & Xie, Zhuan & Huang, Yiping, 2016. "Roads to innovation: Firm-level evidence from China:," IFPRI discussion papers 1542, International Food Policy Research Institute (IFPRI).
    14. Preusse, Verena & Wollni, Meike, 2021. "Adoption of sustainable agricultural practices in the context of urbanisation and environmental stress – Evidence from farmers in the rural-urban interface of Bangalore, India," 2021 Annual Meeting, August 1-3, Austin, Texas 312690, Agricultural and Applied Economics Association.
    15. Luiz Paulo Fávero & Joseph F. Hair & Rafael de Freitas Souza & Matheus Albergaria & Talles V. Brugni, 2021. "Zero-Inflated Generalized Linear Mixed Models: A Better Way to Understand Data Relationships," Mathematics, MDPI, vol. 9(10), pages 1-28, May.
    16. Bono, Pierre-Henri & David, Quentin & Desbordes, Rodolphe & Py, Loriane, 2022. "Metro infrastructure and metropolitan attractiveness," Regional Science and Urban Economics, Elsevier, vol. 93(C).
    17. Scott, Ryan P. & Scott, Tyler A., 2019. "Investing in collaboration for safety: Assessing grants to states for oil and gas distribution pipeline safety program enhancement," Energy Policy, Elsevier, vol. 124(C), pages 332-345.
    18. Riccardo (Jack) Lucchetti & Luca Pedini, 2020. "ParMA: Parallelised Bayesian Model Averaging for Generalised Linear Models," Working Papers 2020:28, Department of Economics, University of Venice "Ca' Foscari".
    19. Gauss Cordeiro & Josemar Rodrigues & Mário Castro, 2012. "The exponential COM-Poisson distribution," Statistical Papers, Springer, vol. 53(3), pages 653-664, August.
    20. Landry, Craig E. & Shonkwiler, J. Scott & Whitehead, John C., 2020. "Economic Values of Coastal Erosion Management: Joint Estimation of Use and Existence Values with recreation demand and contingent valuation data," Journal of Environmental Economics and Management, Elsevier, vol. 103(C).

    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:gam:jmathe:v:11:y:2023:i:6:p:1478-:d:1100540. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.