Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution
Article Type
Research Article
Publication Title
Soft Computing
Abstract
In this article, we draw inferences on stress–strength reliability in a multicomponent system with non-identical strength components based on the progressively censored data from the Kumaraswamy distribution (KuD). When one shape parameter of KuD is known, the uniformly minimum variance unbiased estimator is produced. To evaluate the reliability of such systems when all the parameters are unknown, the maximum likelihood and Bayes estimators are developed. Along with coverage probabilities, the asymptotic confidence and highest posterior credible (HPD) intervals are also obtained. Tierney–Kadane’s approximation and Markov chain Monte Carlo methods are used for Bayesian computations. To compare the performance of estimators, a Monte Carlo simulation study is performed. Also, one real-life example is analyzed for illustrative purposes.
First Page
9317
Last Page
9339
DOI
10.1007/s00500-024-09674-3
Publication Date
9-1-2024
Recommended Citation
Saini, Shubham; Patel, Jyoti; and Garg, Renu, "Statistical inference on multicomponent stress–strength reliability with non-identical component strengths using progressively censored data from Kumaraswamy distribution" (2024). Journal Articles. 5106.
https://digitalcommons.isical.ac.in/journal-articles/5106