  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.
