Multi-component stress–strength reliability estimation under middle censoring scheme

{In this paper, statistical inference is developed for the multi-component stressstrength reliability under middle censoring scheme.} Stress-strength reliability models are crucial in quantifying the probability that a system under stress continues to function without failure. In life-testing experiments, observations are often censored due to time or resource constraints. Although various censoring schemes have been widely studied in the literature, middle censoring remains relatively under-explored despite its practical relevance. This paper presents the inferential framework for estimating the multicomponent stress-strength reliability under middle censoring scheme. We consider a generalized lifetime model and estimate its parameters using classical and Bayesian approaches. Classical inference includes maximum likelihood (ML) and various interval estimation procedures, while the Bayesian inference is performed via Markov Chain Monte Carlo (MCMC) under a generalized entropy loss function (GELF). Extensive simulations are carried out to examine the performance of the suggested approaches, and a real-data illustration demonstrates their practical utility.