IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v35y1997i4p345-354.html
   My bibliography  Save this article

On Bayesian estimation of the multiple decrement function in the competing risks problem, II: The discrete case

Author

Listed:
  • Neath, Andrew A.
  • Samaniego, Francisco J.

Abstract

In an earlier paper, the authors studied the behavior of Bayes estimators of the multiple decrement function in the competing risks problem using a Dirichlet process prior with a continuous prior measure [alpha]. Certain applications, including the analysis of life tables based on grouped data, require a complementary analysis with a discrete [alpha]. Such an analysis is developed here, and applied to provide Bayes estimators of the multiple decrement function from discrete data on the survival times of multiple myeloma patients.

Suggested Citation

  • Neath, Andrew A. & Samaniego, Francisco J., 1997. "On Bayesian estimation of the multiple decrement function in the competing risks problem, II: The discrete case," Statistics & Probability Letters, Elsevier, vol. 35(4), pages 345-354, November.
    • Handle: RePEc:eee:stapro:v:35:y:1997:i:4:p:345-354
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(97)00032-1
    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. Blum, J. & Susarla, V., 1977. "On the posterior distribution of a dirichlet process given randomly right censored observations," Stochastic Processes and their Applications, Elsevier, vol. 5(3), pages 207-211, July.
    2. Neath, Andrew A. & Samaniego, Francisco J., 1996. "On bayesian estimation of the multiple decrement function in the competing risks problem," Statistics & Probability Letters, Elsevier, vol. 31(2), pages 75-83, December.
    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. Antonio Lijoi & Igor Pruenster, 2009. "Models beyond the Dirichlet process," ICER Working Papers - Applied Mathematics Series 23-2009, ICER - International Centre for Economic Research.
    2. Richard L Warr & David H Collins, 2014. "Bayesian nonparametric models for combining heterogeneous reliability data," Journal of Risk and Reliability, , vol. 228(2), pages 166-175, April.
    3. Victor Salinas & José Romeo & Alexis Peña, 2010. "On Bayesian estimation of a survival curve: comparative study and examples," Computational Statistics, Springer, vol. 25(3), pages 375-389, September.
    4. Dean A. Follmann & Paul S. Albert, 1999. "Bayesian Monitoring of Event Rates with Censored Data," Biometrics, The International Biometric Society, vol. 55(2), pages 603-607, June.
    5. Gary L. Rosner, 2005. "Bayesian Monitoring of Clinical Trials with Failure-Time Endpoints," Biometrics, The International Biometric Society, vol. 61(1), pages 239-245, March.
    6. Neath, Andrew A. & Samaniego, Francisco J., 1996. "On the distinguished role of the multivariate exponential distribution in Bayesian estimation in competing risks problems," Statistics & Probability Letters, Elsevier, vol. 31(1), pages 69-74, December.
    7. Antonio Lijoi & Igor Prunster, 2009. "Models beyond the Dirichlet process," Quaderni di Dipartimento 103, University of Pavia, Department of Economics and Quantitative Methods.
    8. Chiara Gigliarano & Pietro Muliere, 2013. "Estimating the Lorenz curve and Gini index with right censored data: a Polya tree approach," METRON, Springer;Sapienza Università di Roma, vol. 71(2), pages 105-122, September.
    9. Chenyang Zhang & Guosheng Yin, 2023. "Bayesian nonparametric analysis of restricted mean survival time," Biometrics, The International Biometric Society, vol. 79(2), pages 1383-1396, June.
    10. Ghosh, J. K. & Ramamoorthi, R. V. & Srikanth, K. R., 1999. "Bayesian analysis of censored data," Statistics & Probability Letters, Elsevier, vol. 41(3), pages 255-265, February.

    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:stapro:v:35:y:1997:i:4:p:345-354. 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.