Reliability Estimation in a Multicomponent Stress-Strength Model Based on Inverse Weibull Distribution

Raj Kamal Maurya, Yogesh Mani Tripathi () and Tanmay Kayal
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Raj Kamal Maurya: Sardar Vallabhbhai National Institute of Technology
Yogesh Mani Tripathi: Indian Institute of Technology Patna
Tanmay Kayal: neurIOT Technologies LLP

Sankhya B: The Indian Journal of Statistics, 2022, vol. 84, issue 1, No 14, 364-401

Abstract: Abstract Reliability inference in a multicomponent stress-strength (MSS) model is studied when components are exposed to a specific random stress. Stress and strength variables are assumed to follow inverse Weibull distributions with different scale and same shape parameter. A s-out-of-k:G system fails if s or more components simultaneously become inoperative. Different estimates of MSS reliability are obtained from frequentist and Bayesian viewpoint. In particular Bayes estimates are evaluated from Lindley method and Metropolis-Hastings algorithm. Unbiased estimation is also considered when shape parameter is known. We construct asymptotic intervals and obtain corresponding coverage probabilities using observed information matrix. In sequel credible intervals are also obtained. A simulation study is performed to examine the estimated risks of proposed estimation methods and analyze two numerical examples from application viewpoint. Finally, optimal plans are discussed for the multicomponent system.

Keywords: Bayes estimate; Highest posterior density interval; Likelihood estimation; MSS model; Reliability; Primary 62F10, 62F15; Secondary 62N05 (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13571-021-00263-0

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