  In this paper, a competing risks model is analyzed based on improved adaptive
type-II progressive censored sample (IAT-II PCS). Two independent competing
causes of failures are considered. It is assumed that lifetimes of the
competing causes of failure follow exponential distributions with different
means. Maximum likelihood estimators (MLEs) for the unknown model parameters
are obtained. Using asymptotic normality property of MLE, the asymptotic
confidence intervals are constructed. Existence and uniqueness properties of
the MLEs are studied. Further, bootstrap confidence intervals are computed. The
Bayes estimators are obtained under symmetric and asymmetric loss functions
with non-informative and informative priors. For informative priors,
independent gamma distributions are considered. Highest posterior density (HPD)
credible intervals are obtained. A Monte Carlo simulation study is carried out
to compare performance of the established estimates. Furthermore, three
different optimality criteria are proposed to obtain the optimal censoring
plan. Finally, a real-life data set is considered for illustrative purposes.
