On multicomponent stress-strength reliability for progressively censored logistic exponential model.

The recent era of technological revolution led to the design and development of reliable products with multiple components for multiple functionality which produced a tough situation for manufacturers to test reliability of such products before launching their products to the real world market. This paper considers reliability estimation for a multicomponent stress-strength (MSS) model under progressively censored data. The classical and Bayesian estimation procedures are employed to evaluate point and interval estimators of the reliability when the failure pattern of the stress and strength components are modeled using the highly flexible logistic exponential distributions with a common shape parameter. When this common parameter is unknown, maximum likelihood estimators of MSS reliability are derived, and the corresponding asymptotic interval is also developed. Further Bayesian point estimators are obtained using two different techniques namely Lindley approximation and Markov chain Monte Carlo. In sequel credible intervals are also constructed. Similar inferences for the considered reliability are also derived having common shape parameter known. Further, an extensive simulation study is conducted and observations are noted. At the end, to illustrate the proposed methods, a real data set is also analyzed.