Statistical analysis of dependent competing risks model in constant stress accelerated life testing with progressive censoring based on copula function

Abstract

In this paper, we consider the statistical analysis for the dependent competing risks model in the constant stress accelerated life testing with Type-II progressive censoring. It is focused on two competing risks from Lomax distribution. The maximum likelihood estimators of the unknown parameters, the acceleration coefficients and the reliability of unit are obtained by using the Bivariate Pareto Copula function and the measure of dependence known as Kendall's tau. In addition, the 95% confidence intervals as well as the coverage percentages are obtained by using Bootstrap-p and Bootstrap-t method. Then, a simulation study is carried out by the Monte Carlo method for different measures of Kendall's tau and different testing schemes. Finally, a real competing risks data is analysed for illustrative purposes. The results indicate that using copula function to deal with the dependent competing risks problems is effective and feasible.

Keywords

• dependent competing risks
• bivariate pareto copula
• kendall's tau
• bootstrap method
• constant stress accelerated life testing
• maximum likelihood estimators