IDEAS home Printed from https://ideas.repec.org/a/spr/compst/v29y2014i5p959-980.html
   My bibliography  Save this article

On the zero-modified poisson model: Bayesian analysis and posterior divergence measure

Author

Listed:
  • Katiane Conceição
  • Marinho Andrade
  • Francisco Louzada

Abstract

In this paper we consider a Bayesian approach for the zero-modified Poisson distribution, which is recommended for fitting count data which shows any modification related to the frequency of zero. However, some loss may occur when we have the knowledge that the datasets show no modification in the zero frequency and has the necessary conditions for the assumption of a Poisson distribution, and still considers the zero-modified Poisson distribution. In this context, we propose the use of the Kullback–Leibler divergence measure to evaluate this loss. The proposed methodology was illustrated in simulated datasets, whose results were able to evaluate the losses and establish its relationship with the Kullback–Leibler divergence measure. Moreover, we exemplify the use of the methodology by considering two real datasets. Copyright Springer-Verlag Berlin Heidelberg 2014

Suggested Citation

  • Katiane Conceição & Marinho Andrade & Francisco Louzada, 2014. "On the zero-modified poisson model: Bayesian analysis and posterior divergence measure," Computational Statistics, Springer, vol. 29(5), pages 959-980, October.
  • Handle: RePEc:spr:compst:v:29:y:2014:i:5:p:959-980
    DOI: 10.1007/s00180-013-0473-y
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s00180-013-0473-y
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s00180-013-0473-y?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. Dalrymple, M. L. & Hudson, I. L. & Ford, R. P. K., 2003. "Finite Mixture, Zero-inflated Poisson and Hurdle models with application to SIDS," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 491-504, January.
    2. Grogger, J T & Carson, Richard T, 1991. "Models for Truncated Counts," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 6(3), pages 225-238, July-Sept.
    3. Hassan Bakouch & Miroslav Ristić, 2010. "Zero truncated Poisson integer-valued AR(1) model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(2), pages 265-280, September.
    4. 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.
    5. Dietz, Ekkehart & Bohning, Dankmar, 2000. "On estimation of the Poisson parameter in zero-modified Poisson models," Computational Statistics & Data Analysis, Elsevier, vol. 34(4), pages 441-459, October.
    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. 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.
    2. Lim, Hwa Kyung & Song, Juwon & Jung, Byoung Cheol, 2013. "Score tests for zero-inflation and overdispersion in two-level count data," Computational Statistics & Data Analysis, Elsevier, vol. 61(C), pages 67-82.
    3. 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.
    4. 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.
    5. Wai Soe Zin & Aya Suzuki & Kelvin S.-H. Peh & Alexandros Gasparatos, 2019. "Economic Value of Cultural Ecosystem Services from Recreation in Popa Mountain National Park, Myanmar: A Comparison of Two Rapid Valuation Techniques," Land, MDPI, vol. 8(12), pages 1-20, December.
    6. 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.
    7. Bowker, James Michael & Starbuck, C. Meghan & English, Donald B.K. & Bergstrom, John C. & Rosenberger, Randall S. & McCollum, Daniel W., 2009. "Estimating the Net Economic Value of National Forest Recreation: An Application of the National Visitor Use Monitoring Database," Faculty Series 59603, University of Georgia, Department of Agricultural and Applied Economics.
    8. 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.
    9. Hélène Huber, 2006. "Decomposing the causes of health care use inequalities: a micro-simulations approach," Working Papers hal-04138520, HAL.
    10. Cho, Daegon & Hwang, Youngdeok & Park, Jongwon, 2018. "More buzz, more vibes: Impact of social media on concert distribution," Journal of Economic Behavior & Organization, Elsevier, vol. 156(C), pages 103-113.
    11. 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.
    12. Greene, William, 2007. "Functional Form and Heterogeneity in Models for Count Data," Foundations and Trends(R) in Econometrics, now publishers, vol. 1(2), pages 113-218, August.
    13. Das, Ujjwal & Das, Kalyan, 2018. "Inference on zero inflated ordinal models with semiparametric link," Computational Statistics & Data Analysis, Elsevier, vol. 128(C), pages 104-115.
    14. Niklas Elert, 2014. "What determines entry? Evidence from Sweden," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 53(1), pages 55-92, August.
    15. Sarah Brown & Pulak Ghosh & Bhuvanesh Pareek & Karl Taylor, 2017. "Financial Hardship and Saving Behaviour: Bayesian Analysis of British Panel Data," Working Papers 2017011, The University of Sheffield, Department of Economics.
    16. Egan, Kevin & Herriges, Joseph, 2006. "Multivariate count data regression models with individual panel data from an on-site sample," Journal of Environmental Economics and Management, Elsevier, vol. 52(2), pages 567-581, September.
    17. Dhakal, Bhubaneswor & Yao, Richard T. & Turner, James A. & Barnard, Tim, 2012. "Recreational users' willingness to pay and preferences for changes in planted forest features," Forest Policy and Economics, Elsevier, vol. 17(C), pages 34-44.
    18. Gurmu, Shiferaw & Rilstone, Paul & Stern, Steven, 1998. "Semiparametric estimation of count regression models1," Journal of Econometrics, Elsevier, vol. 88(1), pages 123-150, November.
    19. Yanling Li & Zita Oravecz & Shuai Zhou & Yosef Bodovski & Ian J. Barnett & Guangqing Chi & Yuan Zhou & Naomi P. Friedman & Scott I. Vrieze & Sy-Miin Chow, 2022. "Bayesian Forecasting with a Regime-Switching Zero-Inflated Multilevel Poisson Regression Model: An Application to Adolescent Alcohol Use with Spatial Covariates," Psychometrika, Springer;The Psychometric Society, vol. 87(2), pages 376-402, June.
    20. John A. Curtis, 2002. "Estimating the Demand for Salmon Angling in Ireland," The Economic and Social Review, Economic and Social Studies, vol. 33(3), pages 319-332.

    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:spr:compst:v:29:y:2014:i:5:p:959-980. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.