Analysis of the improved adaptive type-II progressive censoring based on competing risk data
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In this paper, a competing risk model is analyzed based on the improved adaptive type-II progressive censored sample (IAT-II PCS). Only two competing causes of failures from independent exponential distributions are considered. Maximum likelihood estimates (MLEs) for the unknown model parameters are obtained. By using the asymptotic normality properties of the MLEs, the approximate confidence intervals are constructed. The existence and uniqueness of the MLEs are studied. The Bayes estimates are obtained under the symmetric and asymmetric loss functions with non-informative and independent gamma prior distributions. Further, the highest posterior density (HPD) credible intervals are obtained by using the MCMC technique. Coverage probabilities for each confidence interval are computed. A Monte Carlo simulation study is carried out to compare the performance of the proposed estimates. Finally, a real data set is considered for illustrative purposes.
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