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In this article, we obtain maximum likelihood and Bayes estimates of the parameters, reliability and hazard functions for generalized Rayleigh distribution when progressive type-II censored sample is available. Bayes estimates are derived under three loss functions: squared error, LINEX and generalized entropy. It is assumed that the parameters have independent gamma prior distributions. The estimates cannot be obtained in closed form, and hence the method of Lindley’s approximation is employed in obtaining the desired Bayes estimates. The highest posterior density credible intervals of the model parameters are computed using importance sampling procedure. Moreover, approximate confidence intervals are constructed based on the normal approximation to maximum likelihood estimate and log-transformed maximum likelihood estimate. In order to construct the asymptotic confidence interval of the reliability and hazard functions, it is required to find their variances. These are approximated by delta method. A numerical study is performed to compare the proposed estimates with respect to their average values and mean squared error using Monte Carlo simulations. Further, based on the asymptotic normality of the maximum likelihood estimates, we provide the coverage probabilities for some defined pivotal quantities for model parameters. Finally, a real life dataset is considered to compute the proposed estimates.
Maiti et al. (2019) studied this question.