IDEAS home Printed from https://ideas.repec.org/p/crs/wpaper/2004-06.html
   My bibliography  Save this paper

Bootstrap Confidence Intervals in Mixtures of Discrete Distributions

Author

Listed:
  • Dimitri Karlis

    (Crest)

  • Valentin Patilea

    (Crest)

Abstract

The problem of building bootstrap con¯dence intervals for small probabilitieswith count data is considered. The true probability distribution generating the in-dependent observations is supposed to be a mixture of a given family of power seriesdistributions. The mixing distribution is estimated by nonparametric maximum like-lihood and the corresponding mixture is used for resampling. We build percentile¡tand Efron percentile bootstrap con¯dence intervals for the probabilities and we provetheir consistency in probability. The theoretical results are supported by simulationexperiments for Poisson and Geometric mixtures. We compare percentile¡t andEfron percentile bootstrap intervals with other eight bootstrap or asymptotic theorybased intervals. It appears that Efron percentile bootstrap interval outperforms thecompetitors in terms of coverage probability and length.

Suggested Citation

  • Dimitri Karlis & Valentin Patilea, 2004. "Bootstrap Confidence Intervals in Mixtures of Discrete Distributions," Working Papers 2004-06, Center for Research in Economics and Statistics.
    • Handle: RePEc:crs:wpaper:2004-06
    as

    Download full text from publisher

    File URL: http://crest.science/RePEc/wpstorage/2004-06.pdf
    File Function: Crest working paper version
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Theofanis Sapatinas, 1995. "Identifiability of mixtures of power-series distributions and related characterizations," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(3), pages 447-459, September.
    2. SIMAR, Leopold, 1976. "Maximum likelihood estimation of a compound Poisson process," LIDAM Reprints CORE 271, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    3. Rudolf Beran, 1997. "Diagnosing Bootstrap Success," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(1), pages 1-24, March.
    4. J.F. Walhin, & Paris, J., 1999. "Using Mixed Poisson Processes in Connection with Bonus-Malus Systems1," ASTIN Bulletin, Cambridge University Press, vol. 29(1), pages 81-99, May.
    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. Karlis, Dimitris & Patilea, Valentin, 2007. "Confidence intervals of the hazard rate function for discrete distributions using mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5388-5401, July.

    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. 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.
    2. Tzougas, George & Karlis, Dimitris & Frangos, Nicholas, 2017. "Confidence intervals of the premiums of optimal Bonus Malus Systems," LSE Research Online Documents on Economics 70926, London School of Economics and Political Science, LSE Library.
    3. Alfò, Marco & Carbonari, Lorenzo & Trovato, Giovanni, 2023. "On the effects of taxation on growth: an empirical assessment," Macroeconomic Dynamics, Cambridge University Press, vol. 27(5), pages 1289-1318, July.
    4. repec:cte:wsrepe:9857 is not listed on IDEAS
    5. Andersson, Thomas & Brännäs, Kurt, 1991. "Explaining Cross-Country Variation in Nationalization Frequencies," Working Paper Series 319, Research Institute of Industrial Economics.
    6. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
    7. Seungchul Baek & Junyong Park, 2022. "A computationally efficient approach to estimating species richness and rarefaction curve," Computational Statistics, Springer, vol. 37(4), pages 1919-1941, September.
    8. Kitagawa, Toru & Montiel Olea, José Luis & Payne, Jonathan & Velez, Amilcar, 2020. "Posterior distribution of nondifferentiable functions," Journal of Econometrics, Elsevier, vol. 217(1), pages 161-175.
    9. Michel Denuit & Claude Lefèvre & Moshe Shaked, 2000. "Stochastic Convexity of the Poisson Mixture Model," Methodology and Computing in Applied Probability, Springer, vol. 2(3), pages 231-254, September.
    10. Giovanni Angelini & Giuseppe Cavaliere & Luca Fanelli, 2022. "Bootstrap inference and diagnostics in state space models: With applications to dynamic macro models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 37(1), pages 3-22, January.
    11. Zhexiao Lin & Fang Han, 2023. "On the failure of the bootstrap for Chatterjee's rank correlation," Papers 2303.14088, arXiv.org, revised Apr 2023.
    12. Moreira, Marcelo J. & Mourão, Rafael & Moreira, Humberto Ataíde, 2016. "A critical value function approach, with an application to persistent time-series," FGV EPGE Economics Working Papers (Ensaios Economicos da EPGE) 778, EPGE Brazilian School of Economics and Finance - FGV EPGE (Brazil).
    13. Wang, Weizhen, 2013. "A note on bootstrap confidence intervals for proportions," Statistics & Probability Letters, Elsevier, vol. 83(12), pages 2699-2702.
    14. Davidson, Russell & MacKinnon, James G., 2006. "The power of bootstrap and asymptotic tests," Journal of Econometrics, Elsevier, vol. 133(2), pages 421-441, August.
    15. Cavaliere, Giuseppe & Nielsen, Heino Bohn & Pedersen, Rasmus Søndergaard & Rahbek, Anders, 2022. "Bootstrap inference on the boundary of the parameter space, with application to conditional volatility models," Journal of Econometrics, Elsevier, vol. 227(1), pages 241-263.
    16. Becker, Niels G. & Utev, Sergey, 2002. "Multivariate Discrete Distributions with a Product-Type Dependence," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 509-524, November.
    17. Karlis, Dimitris & Patilea, Valentin, 2007. "Confidence intervals of the hazard rate function for discrete distributions using mixtures," Computational Statistics & Data Analysis, Elsevier, vol. 51(11), pages 5388-5401, July.
    18. Yacine Koucha & Alfredo D. Egidio dos Reis, 2021. "Approximations to ultimate ruin probabilities with a Wienner process perturbation," Papers 2107.02537, arXiv.org.
    19. 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.
    20. Pötscher, Benedikt M. & Leeb, Hannes, 2009. "On the distribution of penalized maximum likelihood estimators: The LASSO, SCAD, and thresholding," Journal of Multivariate Analysis, Elsevier, vol. 100(9), pages 2065-2082, October.
    21. Frédéric Lavancier & Arnaud Poinas & Rasmus Waagepetersen, 2021. "Adaptive estimating function inference for nonstationary determinantal point processes," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics;Finnish Statistical Society;Norwegian Statistical Association;Swedish Statistical Association, vol. 48(1), pages 87-107, March.

    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:crs:wpaper:2004-06. 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: Secretariat General (email available below). General contact details of provider: https://edirc.repec.org/data/crestfr.html .

    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.