Statistical inference under joint type-II censored data from two Burr-XII populations

The Burr-XII distribution has been widely applied in engineering, reliability, and survival analysis. Due to its importance, in this article, statistical inferences for Burr-XII distribution under a joint type-II censoring scheme is discussed. The classical likelihood estimation of unknown model parameters is studied via different calculating approaches, such as the expectation-maximization (EM) algorithm and approximate confidence intervals (ACIs) using the observed Fisher information matrix are obtained. The asymptotic bootstrap confidence intervals are also computed. In the sequel, Bayesian estimations of unknown parameters with a gamma prior distribution are considered under squared error, linear-exponential, and generalized entropy loss functions. Subsequently, we calculate the Bayesian credible interval using the importance sample. The performance of the developed methods is investigated through a Monte Carlo simulation study and two real-life examples. The results showed that the proposed estimation strategies have satisfactory results. However, Bayesian approaches were preferable to EM in terms of lower mean square error and higher coverage probability.

Automated summary

In this article, statistical inferences for the Burr-XII distribution under a joint type-II censoring scheme are discussed. Classical likelihood estimation and Bayesian estimations with a gamma prior distribution are studied. The performance of the methods is investigated through simulations and real-life examples. Results suggest that Bayesian approaches outperform the EM algorithm in terms of mean square error and coverage probability.