IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i3d10.1007_s40745-020-00320-x.html
   My bibliography  Save this article

E-Bayesian and Hierarchical Bayesian Estimations for the Inverse Weibull Distribution

Author

Listed:
  • Abdulkareem M. Basheer

    (Damietta University
    Al-Bayda University)

  • H. M. Okasha

    (King AbdulAziz University
    Al-Azhar University)

  • A. H. El-Baz

    (Damietta University)

  • A. M. K. Tarabia

    (Damietta University)

Abstract

In this paper new formulas for E-Bayesian and hierarchical Bayesian estimations of the parameter and reliability of the inverse Weibull distribution are obtained in closed forms. To illustrate the applicability of the obtained results, simulated and real data are used which illustrate that E-Bayesian estimate gives superior performance much better than hierarchical Bayesian for the estimate of the parameter of the inverse Weibull distribution.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-020-00320-x
    DOI: 10.1007/s40745-020-00320-x
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-020-00320-x
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-020-00320-x?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. Bilal Ahmad Para & Tariq Rashid Jan, 2019. "On Three Parameter Discrete Generalized Inverse Weibull Distribution: Properties and Applications," Annals of Data Science, Springer, vol. 6(3), pages 549-570, September.
    2. 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.
    3. 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.
    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. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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.
    6. repec:ibn:ijspnl:v:8:y:2019:i:4:p:25 is not listed on IDEAS
    7. Shubham Agnihotri & Sanjay Kumar Singh & Umesh Singh, 2025. "Bayesian inferences and prediction of exponentiated exponential distribution based on multiple interval censored data," Computational Statistics, Springer, vol. 40(5), pages 2635-2655, June.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Amal S. Hassan & E. A. Elsherpieny & Rokaya E. Mohamed, 2025. "Bayesian Estimation of Stress Strength Modeling Using MCMC Method Based on Outliers," Annals of Data Science, Springer, vol. 12(1), pages 23-62, February.
    17. 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.
    18. Mohammed S. Kotb & Haidy A. Newer & Marwa M. Mohie El-Din, 2024. "Bayesian Inference for the Entropy of the Rayleigh Model Based on Ordered Ranked Set Sampling," Annals of Data Science, Springer, vol. 11(4), pages 1435-1458, August.
    19. 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.
    20. Sarah R. Al-Dawsari & Khalaf S. Sultan, 2021. "Inverted Weibull Regression Models and Their Applications," Stats, MDPI, vol. 4(2), pages 1-22, April.
    21. Manoj Kumar Rastogi & Yogesh Mani Tripathi, 2014. "Estimation for an inverted exponentiated Rayleigh distribution under type II progressive censoring," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(11), pages 2375-2405, November.

    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:spr:aodasc:v:10:y:2023:i:3:d:10.1007_s40745-020-00320-x. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.