IDEAS home Printed from https://ideas.repec.org/a/epw/ejmath/v2y2021i3id14019.html

Bayesian using Importance Sampling Technique of Weibull Regression with Type II Censored Data

Author

Listed:
  • Mohammed Ahmed Al omari

Abstract

Keeping in view the Bayesian approach, the study aims to develop methods through the utilization of Jeffreys prior and modified Jeffreys prior to the covariate obtained by using the Importance sampling technique. For maximum likelihood estimator, covariate parameters, and the shape parameter of Weibull regression distribution with the censored data of Type II will be estimated by the study. It is shown that the obtained estimators in closed forms are not available, but through the usage of appropriate numerical methods, they can be solved. The mean square error is the criterion of comparison. With the use of simulation, performances of these three estimates are assessed, bearing in mind different censored percentages, and various sizes of the sample.

Suggested Citation

  • Mohammed Ahmed Al omari, 2021. "Bayesian using Importance Sampling Technique of Weibull Regression with Type II Censored Data," European Journal of Mathematics and Statistics, European Open Science, vol. 2(3), pages 10-18, June.
    • Handle: RePEc:epw:ejmath:v:2:y:2021:i:3:id:14019
    DOI: 10.24018/ejmath.2021.2.3.19
    as

    Download full text from publisher

    File URL: https://eu-opensci.org/index.php/ejmath/article/view/14019
    File Function: Abstract page
    Download Restriction: no
    File URL: https://eu-opensci.org/index.php/ejmath/article/download/14019/3147
    File Function: Full text
    Download Restriction: no
    File URL: https://libkey.io/10.24018/ejmath.2021.2.3.19?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
    ---><---

    References listed on IDEAS

    as
    1. Singh, Umesh & Gupta, Pramod K. & Upadhyay, S. K., 2005. "Estimation of parameters for exponentiated-Weibull family under type-II censoring scheme," Computational Statistics & Data Analysis, Elsevier, vol. 48(3), pages 509-523, March.
    2. Chen, Ming-Hui & Ibrahim, Joseph G. & Kim, Sungduk, 2008. "Properties and Implementation of Jeffreys’s Prior in Binomial Regression Models," Journal of the American Statistical Association, American Statistical Association, vol. 103(484), pages 1659-1664.
    3. Hahn, Jinyong, 2004. "Does Jeffrey's prior alleviate the incidental parameter problem?," Economics Letters, Elsevier, vol. 82(1), pages 135-138, January.
    4. 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)

    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. 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.
    2. 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.
    3. Kundu, Debasis & Gupta, Rameshwar D., 2008. "Generalized exponential distribution: Bayesian estimations," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1873-1883, January.
    4. 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.
    5. Mani Suleiman & Haydar Demirhan & Leanne Boyd & Federico Girosi & Vural Aksakalli, 2019. "Bayesian logistic regression approaches to predict incorrect DRG assignment," Health Care Management Science, Springer, vol. 22(2), pages 364-375, June.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. Ahmed T. Farhat & Dina A. Ramadan & Hanan Haj Ahmad & Beih S. El-Desouky, 2025. "Statistical Inference for the Modified Fréchet-Lomax Exponential Distribution Under Constant-Stress PALT with Progressive First-Failure Censoring," Mathematics, MDPI, vol. 13(16), pages 1-28, August.
    12. Sarah R. Al-Dawsari & Khalaf S. Sultan, 2021. "Inverted Weibull Regression Models and Their Applications," Stats, MDPI, vol. 4(2), pages 1-22, April.
    13. 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.
    14. 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.
    15. 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.
    16. 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.
    17. 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.
    18. 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.
    19. 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.
    20. Maha A. Aldahlan & Rana A. Bakoban & Leena S. Alzahrani, 2022. "On Estimating the Parameters of the Beta Inverted Exponential Distribution under Type-II Censored Samples," Mathematics, MDPI, vol. 10(3), pages 1-37, February.

    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:epw:ejmath:v:2:y:2021:i:3:id:14019. 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: Support Team (email available below). General contact details of provider: https://eu-opensci.org/index.php/ejmath .

    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.