In this article we consider statistical inferences about the unknown parameters of the exponentiated
Nadarajah-Haghighi (ENH) distribution based on progressively type-II censoring using
classical and Bayesian procedures. For classical procedures, maximum likelihood (ML) and
least square estimators of the unknown parameters are derived. The Bayes estimators are obtained
based on both the symmetric (squared error) and asymmetric (LINEX, general entropy)
loss functions. Furthermore, Markov chain Monte Carlo (MCMC) technique is used to compute
the Bayes estimators and the associated credible intervals. Moreover, asymptotic confidence
intervals are constructed using the normality property of the ML estimates. After that, the asymptotic
confidence intervals using ML estimates and two parametric bootstrap confidence intervals
are provided to compare with Bayes credible intervals. Finally, simulation study and a bladder
cancer application are presented to illustrate the proposed methods of estimation