Title: Estimating the stress-strength parameter in multi-component systems based on adaptive hybrid progressive censoring

Authors: Akram Kohansal; Shirin Shoaee; Mohammad Z. Raqab

Addresses: Department of Statistics, Imam Khomeini International University, Qazvin, Iran ' Department of Actuarial Science, Faculty of Mathematical Sciences, Shahid Beheshti University, Tehran, Iran ' Department of Mathematics, The University of Jordan, Amman 11942, Jordan; King Abdulaziz University, Jeddah, Saudi Arabia

Abstract: Under different probability distributions, numerous authors have discussed the estimation of the reliability in a stress-strength model. In this study, we investigate the reliability parameter estimation in multi-component stress-strength models based on the adaptive hybrid progressive censored sample of two-parameter Kumaraswamy distribution in various situations. In this regard, various methods such as the maximum likelihood, approximate maximum likelihood, Lindley's Bayesian, and Metropolis-Hastings methods are used to estimate the reliability parameter in this structure. Furthermore, the corresponding confidence intervals, bootstrap confidence intervals, and highest posterior density credible intervals of the multi-component reliability parameter are then established. Also, simulation studies are represented to evaluate and compare the performance of the proposed methods and one practical dataset to analyse illustrative purposes.

Keywords: adaptive type-II hybrid censored sample; Bayesian estimation; Kumaraswamy distribution; Monte Carlo simulation; multi-component stress-strength model; progressive censored sample.

DOI: 10.1504/IJISE.2020.10039570

International Journal of Industrial and Systems Engineering, 2022 Vol.41 No.3, pp.363 - 403

Received: 15 Jun 2020
Accepted: 05 Sep 2020

Published online: 12 Jul 2022 *

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