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In this paper, a competing risks model is analyzed based on improved adaptivetype-II progressive censored sample (IAT-II PCS). Two independent competingcauses of failures are considered. It is assumed that lifetimes of thecompeting causes of failure follow exponential distributions with differentmeans. Maximum likelihood estimators (MLEs) for the unknown model parametersare obtained. Using asymptotic normality property of MLE, the asymptoticconfidence intervals are constructed. Existence and uniqueness properties ofthe MLEs are studied. Further, bootstrap confidence intervals are computed. TheBayes estimators are obtained under symmetric and asymmetric loss functionswith 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 outto compare performance of the established estimates. Furthermore, threedifferent optimality criteria are proposed to obtain the optimal censoringplan. Finally, a real-life data set is considered for illustrative purposes.

Inference of a competing risks model with partially observed failure causes under improved adaptive type-II progressive censoring