Statistical inference for two Lindley populations under balanced joint progressive type-II censoring scheme

In order to conduct a comparative lifetime experiment in life testing and reliability theory, the joint censoring scheme has received immense popularity in the last decade. Recently, a new improved joint progressive censoring scheme has been introduced in statistical literature, known as balanced joint progressive censoring scheme. The present study deals with the statistical inferences for the balanced jointly progressive type-II censored two Lindley populations. Maximum likelihood estimators of the model parameters are derived and construction of the asymptotic confidence intervals based on the observed Fisher information matrix is discussed. From the Bayesian point of view, the posterior estimates of the unknown model parameters are calculated assuming the informative priors. A numerical study is carried out to evaluate the efficiency and performance of the proposed estimates. A real data set is analyzed to exemplify all the estimation techniques. Lastly, the criteria for an optimum censoring scheme are given.