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On reliability estimation of Nadarajah–Haghighi distribution under adaptive type‐I progressive hybrid censoring scheme

In this paper, the maximum likelihood and Bayesian estimation methods are considered to estimate the unknown parameters, reliability, and hazard rate functions of the Nadarajah–Haghighi (NH) distribution based on adaptive type‐I progressive hybrid censoring (ATIPHC) scheme. The Bayesian estimation is obtained under the assumption of independent gamma priors. The Bayes estimators cannot be obtained in closed form, therefore Lindley's approximation and Markov chain Monte Carlo (MCMC) method are used to solve this problem. The approximate confidence (ACIs) and credible intervals are also considered. To compare the efficiency of the different proposed estimators, a simulation study is considered and the performance of the different estimators are compared using mean square error (MSE) and interval length criterion. Finally, one real data set is analyzed in order to show how these mentioned estimators can be applied in practice. The simulation and real data analysis showed that the Bayes estimates have the smallest MSEs and the BCIs have the smallest lengths compared to their conventional counterparts.