Statistical Inference of Multi-component Stress-strength Model Based on Kumaraswamy Distribution
HE Fei
CAI Jing
HE Jian
HAN Rong
Abstract：In order to study the reliability of multi-component stress-strength model in series system,based on Kumaraswamy distribution,the maximum likelihood estimation(MLE)of parameters and stress-strength model reliability was given by maximum likelihood method.The Jeffreys criterion was used to construct the uninformative prior distribution,and the Markov chain Monte Carlo(MCMC)method was used to give the Bayesian estimation of the parameters and the stress-strength model reliability.Finally,the inverse moment estimation(IME)of the parameters and the stress-strength model reliability was given by the inverse moment method.The numerical simulation results showed that under different system reliability and different sample sizes,by comparing the values of the three estimation methods,the Bayesian estimation was the best,and IME was better than MLE.This study provided a certain degree of theoretical basis for exploring the reliability analysis of multi-component stress-strength model under series system.
Keywords：Kumaraswamy distributionmulti-component stress-strength modelnon-informative priorinverse moment estimation(IME)series systemMH algorithm
Publication Date：2024-09-20
Online Publishing Date：2025-08-15(First online date of this platform, not the publication date of the document)
Pages：7( 435-441 )