Classical and Bayesian estimation of multicomponent stress-strength reliability for exponentiated Pareto distribution

Akgul F. G.

SOFT COMPUTING, vol.25, no.14, pp.9185-9197, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 25 Issue: 14
  • Publication Date: 2021
  • Doi Number: 10.1007/s00500-021-05902-2
  • Journal Name: SOFT COMPUTING
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Applied Science & Technology Source, Compendex, Computer & Applied Sciences, INSPEC, zbMATH
  • Page Numbers: pp.9185-9197
  • Keywords: Multicomponent stress-strength model, Exponentiated pareto distribution, Maximum likelihood estimation, Bayesian estimation, Monte Carlo simulation, MODEL
  • Karadeniz Technical University Affiliated: No

Abstract

This study deals with the classical and Bayesian estimation of reliability in a multicomponent stress-strength model by assuming that both stress and strength variables follow exponentiated Pareto distribution. First, the maximum likelihood method is used to estimate reliability. The asymptotic confidence interval is constructed. We also propose two bootstrap confidence intervals. Next, the Bayesian estimates of reliability are obtained using Lindley's approximation, Tierney-Kadane approximation and the Markov chain Monte Carlo (MCMC) method since there are no explicit forms. The MCMC method is used to construct the Bayesian credible interval. A Monte Carlo simulation study is performed to compare the performance of the corresponding methods. Finally, the hydrological data set is analyzed in the application part.