Statistical analysis for competing risks model from a Weibull distribution under progressively hybrid censoring

ABSTRACT

This paper considers the statistical analysis for competing risks model under the Type-I progressively hybrid censoring from a Weibull distribution. We derive the maximum likelihood estimates and the approximate maximum likelihood estimates of the unknown parameters. We then use the bootstrap method to construct the confidence intervals. Based on the non informative prior, a sampling algorithm using the acceptance–rejection sampling method is presented to obtain the Bayes estimates, and Monte Carlo method is employed to construct the highest posterior density credible intervals. The simulation results are provided to show the effectiveness of all the methods discussed here and one data set is analyzed.

KEYWORDS:

• Acceptance–rejection sampling
• Approximate maximum likelihood estimates
• Bootstrap method
• Competing risks model
• Progressively hybrid censoring.