Multicomponent Stress-Strength Model Based on Generalized Progressive Hybrid Censoring Scheme: A Statistical Analysis

• Published in: Entropy (Basel, Switzerland) Vol. 24; no. 5; p. 619
• Main Authors: Ma, Haijing, Yan, Zaizai, Jia, Junmei
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 29-04-2022; MDPI
• Subjects: Asymptotic properties; Bayesian estimation; Confidence intervals; Experiments; Failure; generalized progressive hybrid censoring; Markov chains; Mathematical models; Maximum likelihood estimation; Mechanical engineering; multicomponent stress–strength model; Newton-Raphson method; Parameters; reliability; Reliability analysis; Statistical analysis; Statistical inference; Bayesian estimation; generalized progressive hybrid censoring; multicomponent stress–strength model; maximum likelihood estimation; reliability
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e24050619

The statistical inference of the reliability and parameters of the stress-strength model has received great attention in the field of reliability analysis. When following the generalized progressive hybrid censoring (GPHC) scheme, it is important to discuss the point estimate and interval estimate of the reliability of the multicomponent stress-strength (MSS) model, in which the stress and the strength variables are derived from different distributions by assuming that stress follows the Chen distribution and that strength follows the Gompertz distribution. In the present study, the Newton-Raphson method was adopted to derive the maximum likelihood estimation (MLE) of the model parameters, and the corresponding asymptotic distribution was adopted to construct the asymptotic confidence interval (ACI). Subsequently, the exact confidence interval (ECI) of the parameters was calculated. A hybrid Markov chain Monte Carlo (MCMC) method was adopted to determine the approximate Bayesian estimation (BE) of the unknown parameters and the high posterior density credible interval (HPDCI). A simulation study with the actual dataset was conducted for the BEs with squared error loss function (SELF) and the MLEs of the model parameters and reliability, comparing the bias and mean squares errors (MSE). In addition, the three interval estimates were compared in terms of the average interval length (AIL) and coverage probability (CP).