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

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

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 & Tahir Mehmood, 2023. "Bayesian Estimation of a Geometric Life Testing Model under Different Loss Functions Using a Doubly Type-1Censoring Scheme," Mathematical Problems in Engineering, Hindawi, vol. 2023, pages 1-14, April.
    • Handle: RePEc:hin:jnlmpe:7184528
    DOI: 10.1155/2023/7184528
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    Cited by:

    1. Nadeem Akhtar & Sajjad Ahmad Khan & Emad A. A. Ismail & Fuad A. Awwad & Akbar Ali Khan & Taza Gul & Haifa Alqahtani, 2024. "Analyzing quantitative performance: Bayesian estimation of 3-component mixture geometric distributions based on Kumaraswamy prior," Statistical Papers, Springer, vol. 65(7), pages 4431-4451, September.

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