IDEAS home Printed from https://ideas.repec.org/a/wly/jnlmpe/v2023y2023i1n7184528.html

Bayesian Estimation of a Geometric Life Testing Model under Different Loss Functions Using a Doubly Type‐1Censoring Scheme

Author

Listed:
  • Nadeem Akhtar
  • Sajjad Ahmad Khan
  • Muhammad Amin
  • Akbar Ali Khan
  • Zahra Almaspoor
  • Amjad Ali
  • Sadaf Manzoor

Abstract

In this article, we consider the doubly type‐1 censoring scheme that researchers frequently use in clinical trials and lifetime experiments. The Bayesian paradigm will be used to estimate the parameters of the Geometric Lifetime Model (GLTM) using a doubly type‐I censoring scheme. Bayes estimators and their associated Bayes risks are examined in terms of closed‐form algebraic expressions. This research also includes a strategy for eliciting hyperparameters based on prior prediction distributions. To evaluate the strength and effectiveness of the suggested estimating approach, thorough simulation studies as well as real‐life data analysis are presented. The results depict that Squared Error Loss Function (SELF) is more efficient, and the Beta prior is suitable while estimating the parameter of GLTM.

Suggested Citation

  • Nadeem Akhtar & Sajjad Ahmad Khan & Muhammad Amin & Akbar Ali Khan & Zahra Almaspoor & Amjad Ali & Sadaf Manzoor, 2023. "Bayesian Estimation of a Geometric Life Testing Model under Different Loss Functions Using a Doubly Type‐1Censoring Scheme," Mathematical Problems in Engineering, John Wiley & Sons, vol. 2023(1).
    • Handle: RePEc:wly:jnlmpe:v:2023:y:2023:i:1:n:7184528
    DOI: 10.1155/2023/7184528
    as

    Download full text from publisher

    File URL: https://doi.org/10.1155/2023/7184528
    Download Restriction: no
    File URL: https://libkey.io/10.1155/2023/7184528?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. Hare Krishna & Neha Goel, 2017. "Maximum Likelihood and Bayes Estimation in Randomly Censored Geometric Distribution," Journal of Probability and Statistics, Hindawi, vol. 2017, pages 1-12, February.
    2. Arturo J. fernández & José I. Bravo & Íñigo Fuentes, 2002. "Computing maximum likelihood estimates from type II doubly censored exponential data," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 11(2), pages 187-200, 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. Neha Goel & Hare Krishna, 2022. "Different methods of estimation in two parameter Geometric distribution with randomly censored data," 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. 13(4), pages 1652-1665, August.
    2. Rashad M. EL-Sagheer & Mohamed S. Eliwa & Khaled M. Alqahtani & Mahmoud EL-Morshedy, 2022. "Asymmetric Randomly Censored Mortality Distribution: Bayesian Framework and Parametric Bootstrap with Application to COVID‐19 Data," Journal of Mathematics, John Wiley & Sons, vol. 2022(1).
    3. Fernandez, Arturo J., 2006. "Bayesian estimation based on trimmed samples from Pareto populations," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 1119-1130, November.
    4. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.
    5. Fernandez, Arturo J., 2006. "Bounding maximum likelihood estimates based on incomplete ordered data," Computational Statistics & Data Analysis, Elsevier, vol. 50(8), pages 2014-2027, April.
    6. Nadeem Akhtar & Muteb Faraj Alharthi, 2025. "Bayesian analysis of heterogeneous data outliers using a censored mixture model," Statistical Papers, Springer, vol. 66(3), pages 1-17, April.
    7. Fernández, Arturo J., 2008. "Reliability inference and sample-size determination under double censoring for some two-parameter models," Computational Statistics & Data Analysis, Elsevier, vol. 52(7), pages 3426-3440, March.
    8. Rameshwar Gupta & Ramesh Gupta, 2008. "Analyzing skewed data by power normal model," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 17(1), pages 197-210, May.
    9. Carlos Pérez-González & Arturo Fernández, 2009. "Accuracy of approximate progressively censored reliability sampling plans for exponential models," Statistical Papers, Springer, vol. 50(1), pages 161-170, January.

    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:wly:jnlmpe:v:2023:y:2023:i:1:n:7184528. 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: Wiley Content Delivery (email available below). General contact details of provider: https://onlinelibrary.wiley.com/journal/2629 .

    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.