In this study, we discussed the Bayesian property of unknown parameter and reliability
characteristic of the Shanker distribution. The maximum likelihood estimate is
calculated. The approximate confidence interval of the unknown parameter is
constructed based on the asymptotic normality of maximum likelihood estimator. Two
bootstrap confidence intervals for the unknown parameter are also computed.
Bayesian estimates of parameter and reliability characteristic against squared error loss
function are obtained. Lindley's approximation and Metropolis-Hastings algorithm are
applied to obtain the Bayes estimates. In consequence, we also construct the highest
posterior density intervals. A numerical comparison is also made to compare different
methods through a Monte Carlo simulation study. Finally, two real data sets are also
analyzed using the proposed methods.