Bayesian and non-Bayesian estimation methods to independent competing risks models with type II half logistic weibull sub-distributions with application to an automatic life test

Abstract

In the survival data analysis, competing risks are commonly overlooked, and conventional statistical methods are used to analyze the event of interest. There may be more than one cause of death or failure in many experimental investigations of survival analysis. A competing risks model will be derived statistically applying Type-II half logistic weibull sub-distributions. Type-II half logistic weibull life?times failure model with independent causes. It is possible to estimate parameters and parametric functions using Bayesian and classical methods. A Bayes estimation is obtained by the Markov chain Monte-Carlo method. The posterior density function and the Metropolis-Hasting algorithm are used to calculate the Markov chain Monte-Carlo samples. Simulation data is used to evaluate the performance of the two methods according to the Type-II censored system. As a test of the discussed model, a real data set is provided.