Reliability Inference for Inverse Power Maxwell Distribution under Progressive Type-II Censored Sample

Document Type : Original Article

Authors
Mohd Irfan
Anup Kumar Sharma
Department of Mathematics, National Institute of Technology Raipur, India.
10.22034/jirss.2025.2023946.1054
Abstract
This paper investigates the reliability and parametric inference for the inverse power Maxwell distribution under progressive Type-II censored sample. Under the frequentist approach, the maximum likelihood estimate, least square, and weighted least square methods are considered for estimating the model parameters and any parametric function involved in this model. Approximate confidence intervals for parameters and any of their functions are created via a variance-covariance matrix. Bayes estimates are obtained using Lindley's approximation and Markov chain Monte Carlo (MCMC) technique under squared error loss function. Additionally, the highest posterior density (HPD) credible intervals are constructed using MCMC approximation techniques. A comprehensive Monte Carlo simulation study is conducted to assess the efficiency of the proposed methodologies. Furthermore, three optimality criteria are presented to choose the most suitable progressive scheme from various sampling plans. The practical utility of the proposed methods is demonstrated using two real-world datasets: the failure times of mechanical components and the strength of glass fiber.
Keywords
Inverse Power Maxwell Distribution
Progressive Type-II Censoring Scheme
Lindley Approximation
Metropolis-Hasting Algorithm
Highest Posterior Density Credible Interval
Optimality
Subjects
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Journal of the Iranian Statistical Society
Volume 23, Issue 2
December 2024
Pages 1-34

  • Receive Date 28 February 2024
  • Revise Date 13 January 2025
  • Accept Date 18 February 2025