In this paper, we discuss the estimation of δ = P(Y < X) based on Type-II progressive

hybrid censored samples when X and Y are two independent Inverse Weibull distributions

with different scale parameters, but having the same shape parameter. Different methods for

estimating δ are applied. The maximum likelihood estimator and the The observed Fisher

information matrix is computed and it is used to construct an asymptotic confidence interval

for δ. Bayes estimate of δ under the assumptions of independent gamma priors. Markov

Chain Monte Carlo (MCMC) technique is used for Bayes computation. Moreover, by using

the MCMC method, we achieve the highest posterior density ( HPD) credible intervals. Monte

Carlo simulations are performed to compare the efficiency of the proposed estimators. One

data analysis has been presented for illustrative purposes.

Keywords: Stress-strength model, Inverse Weibull distribution, maximum likelihood estimator,

Bayes estimator, Markov Chain Monte Carlo,, Type-II progressively hybrid censoring.