IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v41y2003i3-4p491-504.html
   My bibliography  Save this article

Finite Mixture, Zero-inflated Poisson and Hurdle models with application to SIDS

Author

Listed:
  • Dalrymple, M. L.
  • Hudson, I. L.
  • Ford, R. P. K.

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:41:y:2003:i:3-4:p:491-504
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(02)00187-1
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    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. Wang, Peiming & Cockburn, Iain M & Puterman, Martin L, 1998. "Analysis of Patent Data--A Mixed-Poisson-Regression-Model Approach," Journal of Business & Economic Statistics, American Statistical Association, vol. 16(1), pages 27-41, January.
    2. 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.
    3. Peiming Wang & Iain Cockburn & Martin L. Puterman, "undated". "A Mixed Poisson Regression Model for Analysis of Patent Data," Computing in Economics and Finance 1996 _049, Society for Computational Economics.
    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.
    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. Bohning, Dankmar & Seidel, Wilfried, 2003. "Editorial: recent developments in mixture models," Computational Statistics & Data Analysis, Elsevier, vol. 41(3-4), pages 349-357, January.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. M. Tariqul Hasan & Gary Sneddon & Renjun Ma, 2012. "Regression analysis of zero-inflated time-series counts: application to air pollution related emergency room visit data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 39(3), pages 467-476, June.
    7. L. Elbakidze & Y. H. Jin, 2015. "Are Economic Development and Education Improvement Associated with Participation in Transnational Terrorism?," Risk Analysis, John Wiley & Sons, vol. 35(8), pages 1520-1535, 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. 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.
    2. Deb, Partha & Trivedi, Pravin K., 2002. "The structure of demand for health care: latent class versus two-part models," Journal of Health Economics, Elsevier, vol. 21(4), pages 601-625, July.
    3. Payandeh Najafabadi Amir T. & MohammadPour Saeed, 2018. "A k-Inflated Negative Binomial Mixture Regression Model: Application to Rate–Making Systems," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 12(2), pages 1-31, July.
    4. Padmaja Ayyagari & Partha Deb & Jason Fletcher & William Gallo & Jody L. Sindelar, 2013. "Understanding Heterogeneity In Price Elasticities In The Demand For Alcohol For Older Individuals," Health Economics, John Wiley & Sons, Ltd., vol. 22(1), pages 89-105, January.
    5. Chadha, Alka, 2009. "TRIPs and patenting activity: Evidence from the Indian pharmaceutical industry," Economic Modelling, Elsevier, vol. 26(2), pages 499-505, March.
    6. Partha Deb & William T. Gallo & Padmaja Ayyagari & Jason M. Fletcher & Jody L. Sindelar, 2009. "Job Loss: Eat, drink and try to be merry?," NBER Working Papers 15122, National Bureau of Economic Research, Inc.
    7. William Greene, 2007. "Correlation in Bivariate Poisson Regression Model," Working Papers 07-14, New York University, Leonard N. Stern School of Business, Department of Economics.
    8. Herriges, Joseph A. & Phaneuf, Daniel J. & Tobias, Justin L., 2008. "Estimating demand systems when outcomes are correlated counts," Journal of Econometrics, Elsevier, vol. 147(2), pages 282-298, December.
    9. Stephen Hynes & William Greene, 2016. "Preference Heterogeneity in Contingent Behaviour Travel Cost Models with On-site Samples: A Random Parameter vs. a Latent Class Approach," Journal of Agricultural Economics, Wiley Blackwell, vol. 67(2), pages 348-367, June.
    10. Shiferaw Gurmu & Fidel Pérez-Sebastián, 2008. "Patents, R&D and lag effects: evidence from flexible methods for count panel data on manufacturing firms," Empirical Economics, Springer, vol. 35(3), pages 507-526, November.
    11. Padmaja Ayyagari & Partha Deb & Jason Fletcher & William T. Gallo & Jody L. Sindelar, 2009. "Sin Taxes: Do Heterogeneous Responses Undercut Their Value?," NBER Working Papers 15124, National Bureau of Economic Research, Inc.
    12. Bermúdez, Lluís & Karlis, Dimitris, 2012. "A finite mixture of bivariate Poisson regression models with an application to insurance ratemaking," Computational Statistics & Data Analysis, Elsevier, vol. 56(12), pages 3988-3999.
    13. Peiming Wang & Martin Puterman, 1999. "Markov Poisson regression models for discrete time series. Part 1: Methodology," Journal of Applied Statistics, Taylor & Francis Journals, vol. 26(7), pages 855-869.
    14. Eduardo Fé & Richard Hofler, 2013. "Count data stochastic frontier models, with an application to the patents–R&D relationship," Journal of Productivity Analysis, Springer, vol. 39(3), pages 271-284, June.
    15. Dimitris Karlis & Purushottam Papatla & Sudipt Roy, 2016. "Finite mixtures of censored Poisson regression models," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 70(2), pages 100-122, May.
    16. Vincenzo Atella & Francesco Brindisi & Partha Deb & Furio C. Rosati, 2004. "Determinants of access to physician services in Italy: a latent class seemingly unrelated probit approach," Health Economics, John Wiley & Sons, Ltd., vol. 13(7), pages 657-668, July.
    17. Conway, Karen Smith & Deb, Partha, 2005. "Is prenatal care really ineffective? Or, is the 'devil' in the distribution?," Journal of Health Economics, Elsevier, vol. 24(3), pages 489-513, May.
    18. Lim, Hwa Kyung & Li, Wai Keung & Yu, Philip L.H., 2014. "Zero-inflated Poisson regression mixture model," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 151-158.
    19. Drivas, Kyriakos & Economidou, Claire & Tsionas, Efthymios G., 2014. "A Poisson Stochastic Frontier Model with Finite Mixture Structure," MPRA Paper 57485, University Library of Munich, Germany.
    20. Greene, William, 2008. "Functional forms for the negative binomial model for count data," Economics Letters, Elsevier, vol. 99(3), pages 585-590, June.

    More about this item

    Statistics

    Access and download statistics

    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:csdana:v:41:y:2003:i:3-4:p:491-504. 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.elsevier.com/locate/csda .

    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.