Estimation in a multicomponent stress-strength model for progressive censored lognormal distribution

We consider estimation of multicomponent stress-strength reliability under classical as well as Bayesian approaches when stress-strength components follow lognormal distributions and data are observed under progressive censoring. Various estimates are obtained by sequentially considering one parameter common and unknown. Different expressions for the reliability are evaluated under these cases. For both the cases, we obtain different estimates of considered parametric function using likelihood, Lindley and repeated sampling methods. Asymptotic and credible intervals are also obtained. Extensive simulations are conducted to examine the behavior of all estimates under various censoring schemes. We finally present analysis of a real life example from application purposes.