IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v76y2006i15p1675-1684.html

Nonparametric predictive subset selection for proportions

Author

Listed:
  • Coolen, F.P.A.
  • Coolen-Schrijner, P.

Abstract

We present nonparametric predictive lower and upper probabilities for comparison of future numbers of successes in Bernoulli trials, for k[greater-or-equal, slanted]2 independent groups, using proportions data per group. We consider lower and upper probabilities related to subsets of the k groups, both for the event that the selected subset contains the group which gives the highest number of successes in m future trials for each group, and the event that all groups in the selected subset give more successes in m future trials than all not selected groups.

Suggested Citation

  • Coolen, F.P.A. & Coolen-Schrijner, P., 2006. "Nonparametric predictive subset selection for proportions," Statistics & Probability Letters, Elsevier, vol. 76(15), pages 1675-1684, September.
    • Handle: RePEc:eee:stapro:v:76:y:2006:i:15:p:1675-1684
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(06)00122-2
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. J. F. Lawless & Marc Fredette, 2005. "Frequentist prediction intervals and predictive distributions," Biometrika, Biometrika Trust, vol. 92(3), pages 529-542, September.
    2. Coolen, F. P. A., 1996. "Comparing two populations based on low stochastic structure assumptions," Statistics & Probability Letters, Elsevier, vol. 29(4), pages 297-305, September.
    3. David J. Spiegelhalter & Paul Aylin & Nicola G. Best & Stephen J. W. Evans & Gordon D. Murray, 2002. "Commissioned analysis of surgical performance using routine data: lessons from the Bristol inquiry," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 165(2), pages 191-221, June.
    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. P Coolen-Schrijner & F. P. A. Coolen, 2007. "Non-parametric predictive comparison of success-failure data in reliability," Journal of Risk and Reliability, , vol. 221(4), pages 319-327, December.
    2. Stojaković, Mila, 2012. "Set valued probability and its connection with set valued measure," Statistics & Probability Letters, Elsevier, vol. 82(6), pages 1043-1048.
    3. Frank P. A. Coolen & Tahani Coolen-Maturi & Ali M. Y. Mahnashi, 2024. "Nonparametric Predictive Inference for Discrete Lifetime Data," Mathematics, MDPI, vol. 12(22), pages 1-14, November.

    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. Acharki, Naoufal & Bertoncello, Antoine & Garnier, Josselin, 2023. "Robust prediction interval estimation for Gaussian processes by cross-validation method," Computational Statistics & Data Analysis, Elsevier, vol. 178(C).
    2. Coolen-Maturi, Tahani & Elkhafifi, Faiza F. & Coolen, Frank P.A., 2014. "Three-group ROC analysis: A nonparametric predictive approach," Computational Statistics & Data Analysis, Elsevier, vol. 78(C), pages 69-81.
    3. Nicholas Longford & D. B. Rubin, 2006. "Performance assessment and league tables. Comparing like with like," Economics Working Papers 994, Department of Economics and Business, Universitat Pompeu Fabra.
    4. Sheila M. Bird & Cox Sir David & Vern T. Farewell & Goldstein Harvey & Holt Tim & Smith Peter C., 2005. "Performance indicators: good, bad, and ugly," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 168(1), pages 1-27, January.
    5. Jorge Navarro & Francesco Buono, 2023. "Predicting future failure times by using quantile regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(5), pages 543-576, July.
    6. Yuanyuan Shen & Katherine P. Liao & Tianxi Cai, 2015. "Sparse kernel machine regression for ordinal outcomes," Biometrics, The International Biometric Society, vol. 71(1), pages 63-70, March.
    7. Houlding, B. & Coolen, F.P.A., 2012. "Nonparametric predictive utility inference," European Journal of Operational Research, Elsevier, vol. 221(1), pages 222-230.
    8. Trevor C. Bailey & Paul J. Hewson, 2004. "Simultaneous modelling of multiple traffic safety performance indicators by using a multivariate generalized linear mixed model," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(3), pages 501-517, August.
    9. Wang, Hsiuying, 2008. "Coverage probability of prediction intervals for discrete random variables," Computational Statistics & Data Analysis, Elsevier, vol. 53(1), pages 17-26, September.
    10. Vidoni, Paolo, 2015. "Calibrated multivariate distributions for improved conditional prediction," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 16-25.
    11. Haojin Zhou & Tapan Nayak, 2015. "On the equivariance criterion in statistical prediction," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 67(3), pages 541-555, June.
    12. Kubokawa, Tatsuya & Marchand, Éric & Strawderman, William E., 2015. "On predictive density estimation for location families under integrated squared error loss," Journal of Multivariate Analysis, Elsevier, vol. 142(C), pages 57-74.
    13. Paolo Vidoni, 2017. "Improved multivariate prediction regions for Markov process models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 26(1), pages 1-18, March.
    14. Mohammad Z. Raqab & S. F. Bagheri & A. Asgharzadeh & Ahmad N. Alothman, 2025. "Optimal prediction regions of future lifetimes under Type-II censored samples," Statistical Papers, Springer, vol. 66(5), pages 1-25, August.
    15. De Oliveira, Victor & Rui, Changxiang, 2009. "On shortest prediction intervals in log-Gaussian random fields," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4345-4357, October.
    16. V. J. Roelofs & F. P. A. Coolen & A. D. M. Hart, 2011. "Nonparametric Predictive Inference for Exposure Assessment," Risk Analysis, John Wiley & Sons, vol. 31(2), pages 218-227, February.
    17. Qinglong Tian & Daniel J. Nordman & William Q. Meeker, 2022. "Constructing Prediction Intervals Using the Likelihood Ratio Statistic," INFORMS Joural on Data Science, INFORMS, vol. 1(1), pages 63-80, April.
    18. Yang Liu & Ji Seung Yang, 2018. "Interval Estimation of Latent Variable Scores in Item Response Theory," Journal of Educational and Behavioral Statistics, , vol. 43(3), pages 259-285, June.
    19. De Oliveira, Victor & Kone, Bazoumana, 2015. "Prediction intervals for integrals of Gaussian random fields," Computational Statistics & Data Analysis, Elsevier, vol. 83(C), pages 37-51.
    20. Paolo Vidoni, 2009. "A simple procedure for computing improved prediction intervals for autoregressive models," Journal of Time Series Analysis, Wiley Blackwell, vol. 30(6), pages 577-590, November.

    More about this item

    Keywords

    ;

    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:stapro:v:76:y:2006:i:15:p:1675-1684. 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/wps/find/journaldescription.cws_home/622892/description#description .

    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.