Statistical Inferences of Burr XII Lifetime Models under Joint Type-1 Competing Risks Samples

The problem of statistical inference under joint censoring samples has received considerable attention in the past few years. In this paper, we adopted this problem when units under the test fail with different causes of failure which is known by the competing risks model. The model is formulated under consideration that only two independent causes of failure and the unit are collected from two lines of production and its life distributed with Burr XII lifetime distribution. So, under Type-I joint competing risks samples, we obtained the maximum likelihood (ML) and Bayes estimators. Interval estimation is discussed through asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. The numerical computations which described the quality of theoretical results are discussed in the forms of real data analyzed and Monte Carlo simulation study. Finally, numerical results are discussed and listed through some points as a brief comment.

Automated summary

This paper addresses the problem of statistical inference under joint censoring samples using the competing risks model. Maximum likelihood and Bayes estimators are obtained, and interval estimation is discussed through various methods such as asymptotic confidence interval, bootstrap confidence intervals, and Bayes credible interval. Numerical results are discussed and summarized through real data analysis and Monte Carlo simulation study, with key points listed in a brief comment.