IDEAS home Printed from https://ideas.repec.org/a/taf/japsta/v40y2013i7p1597-1607.html
   My bibliography  Save this article

Bayesian estimation of parameters of inverse Weibull distribution

Author

Listed:
  • Sanjay Kumar Singh
  • Umesh Singh
  • Dinesh Kumar

Abstract

The present paper describes the Bayes estimators of parameters of inverse Weibull distribution for complete, type I and type II censored samples under general entropy and squared error loss functions. The proposed estimators have been compared on the basis of their simulated risks (average loss over sample space). A real-life data set is used to illustrate the results.

Suggested Citation

  • Sanjay Kumar Singh & Umesh Singh & Dinesh Kumar, 2013. "Bayesian estimation of parameters of inverse Weibull distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(7), pages 1597-1607, July.
    • Handle: RePEc:taf:japsta:v:40:y:2013:i:7:p:1597-1607
    DOI: 10.1080/02664763.2013.789492
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/02664763.2013.789492
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1080/02664763.2013.789492?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. Kundu, Debasis & Howlader, Hatem, 2010. "Bayesian inference and prediction of the inverse Weibull distribution for Type-II censored data," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1547-1558, 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. Haiping Ren & Xue Hu, 2023. "Bayesian Estimations of Shannon Entropy and Rényi Entropy of Inverse Weibull Distribution," Mathematics, MDPI, vol. 11(11), pages 1-16, May.
    2. Abdulkareem M. Basheer & H. M. Okasha & A. H. El-Baz & A. M. K. Tarabia, 2023. "E-Bayesian and Hierarchical Bayesian Estimations for the Inverse Weibull Distribution," Annals of Data Science, Springer, vol. 10(3), pages 737-759, June.
    3. Sukhdev Singh & Yogesh Mani Tripathi, 2018. "Estimating the parameters of an inverse Weibull distribution under progressive type-I interval censoring," Statistical Papers, Springer, vol. 59(1), pages 21-56, March.
    4. Ibrahim Elbatal & Francesca Condino & Filippo Domma, 2016. "Reflected Generalized Beta Inverse Weibull Distribution: definition and properties," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(2), pages 316-340, November.
    5. Sanku Dey & Tanujit Dey, 2014. "On progressively censored generalized inverted exponential distribution," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(12), pages 2557-2576, December.
    6. Hassan M. Okasha & Abdulkareem M. Basheer & Yuhlong Lio, 2022. "The E-Bayesian Methods for the Inverse Weibull Distribution Rate Parameter Based on Two Types of Error Loss Functions," Mathematics, MDPI, vol. 10(24), pages 1-27, December.

    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. Azeem Ali & Sanku Dey & Haseeb Ur Rehman & Zeeshan Ali, 2019. "On Bayesian reliability estimation of a 1-out-of-k load sharing system model of modified Burr-III distribution," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 10(5), pages 1052-1081, October.
    2. Amel Abd-El-Monem & Mohamed S. Eliwa & Mahmoud El-Morshedy & Afrah Al-Bossly & Rashad M. EL-Sagheer, 2023. "Statistical Analysis and Theoretical Framework for a Partially Accelerated Life Test Model with Progressive First Failure Censoring Utilizing a Power Hazard Distribution," Mathematics, MDPI, vol. 11(20), pages 1-21, October.
    3. Hassan M. Okasha & Abdulkareem M. Basheer & Yuhlong Lio, 2022. "The E-Bayesian Methods for the Inverse Weibull Distribution Rate Parameter Based on Two Types of Error Loss Functions," Mathematics, MDPI, vol. 10(24), pages 1-27, December.
    4. Suparna Basu & Sanjay K. Singh & Umesh Singh, 2019. "Estimation of Inverse Lindley Distribution Using Product of Spacings Function for Hybrid Censored Data," Methodology and Computing in Applied Probability, Springer, vol. 21(4), pages 1377-1394, December.
    5. Kehui Yao & Jun Zhu & Daniel J. O'Brien & Daniel Walsh, 2023. "Bayesian spatio‐temporal survival analysis for all types of censoring with application to a wildlife disease study," Environmetrics, John Wiley & Sons, Ltd., vol. 34(8), December.
    6. Musleh, Rola M. & Helu, Amal, 2014. "Estimation of the inverse Weibull distribution based on progressively censored data: Comparative study," Reliability Engineering and System Safety, Elsevier, vol. 131(C), pages 216-227.
    7. Sarah R. Al-Dawsari & Khalaf S. Sultan, 2021. "Inverted Weibull Regression Models and Their Applications," Stats, MDPI, vol. 4(2), pages 1-22, April.
    8. Tien Thanh Thach & Radim Bris, 2020. "Improved new modified Weibull distribution: A Bayes study using Hamiltonian Monte Carlo simulation," Journal of Risk and Reliability, , vol. 234(3), pages 496-511, June.
    9. Devendra Kumar, 2017. "The Singh–Maddala distribution: properties and estimation," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 8(2), pages 1297-1311, November.
    10. Aisha Fayomi & Ehab M. Almetwally & Maha E. Qura, 2023. "Exploring New Horizons: Advancing Data Analysis in Kidney Patient Infection Rates and UEFA Champions League Scores Using Bivariate Kavya–Manoharan Transformation Family of Distributions," Mathematics, MDPI, vol. 11(13), pages 1-37, July.
    11. Refah Alotaibi & Hoda Rezk & Sanku Dey & Hassan Okasha, 2021. "Bayesian estimation for Dagum distribution based on progressive type I interval censoring," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-17, June.
    12. Haiping Ren & Xue Hu, 2023. "Bayesian Estimations of Shannon Entropy and Rényi Entropy of Inverse Weibull Distribution," Mathematics, MDPI, vol. 11(11), pages 1-16, May.
    13. Indrajeet Kumar & Shishir Kumar Jha & Kapil Kumar, 2023. "On Some Estimation Methods for the Inverse Pareto Distribution," Annals of Data Science, Springer, vol. 10(4), pages 1035-1068, August.
    14. Ibrahim Elbatal & Francesca Condino & Filippo Domma, 2016. "Reflected Generalized Beta Inverse Weibull Distribution: definition and properties," Sankhya B: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 78(2), pages 316-340, November.
    15. Refah Alotaibi & Mazen Nassar & Hoda Rezk & Ahmed Elshahhat, 2022. "Inferences and Engineering Applications of Alpha Power Weibull Distribution Using Progressive Type-II Censoring," Mathematics, MDPI, vol. 10(16), pages 1-21, August.
    16. Suparna Basu & Sanjay Kumar Singh & Umesh Singh, 2017. "Parameter estimation of inverse Lindley distribution for Type-I censored data," Computational Statistics, Springer, vol. 32(1), pages 367-385, March.
    17. Hamdy M. Salem, 2019. "The Marshall–Olkin Generalized Inverse Weibull Distribution: Properties and Application," Modern Applied Science, Canadian Center of Science and Education, vol. 13(2), pages 1-54, February.
    18. Hanieh Panahi & Abdolreza Sayyareh, 2014. "Parameter estimation and prediction of order statistics for the Burr Type XII distribution with Type II censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(1), pages 215-232, January.
    19. Teena Goyal & Piyush K. Rai & Sandeep K. Maurya, 2020. "Bayesian Estimation for GDUS Exponential Distribution Under Type-I Progressive Hybrid Censoring," Annals of Data Science, Springer, vol. 7(2), pages 307-345, June.
    20. Abdulkareem M. Basheer & H. M. Okasha & A. H. El-Baz & A. M. K. Tarabia, 2023. "E-Bayesian and Hierarchical Bayesian Estimations for the Inverse Weibull Distribution," Annals of Data Science, Springer, vol. 10(3), pages 737-759, 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:taf:japsta:v:40:y:2013:i:7:p:1597-1607. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/CJAS20 .

    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.