  In this article, we consider statistical inference based on dependent
competing risks data from Marshall-Olkin bivariate Weibull distribution. The
maximum likelihood estimates of the unknown model parameters have been computed
by using the Newton-Raphson method under adaptive Type II progressive hybrid
censoring with partially observed failure causes. The existence and uniqueness
of maximum likelihood estimates are derived. Approximate confidence intervals
have been constructed via the observed Fisher information matrix using the
asymptotic normality property of the maximum likelihood estimates. Bayes
estimates and highest posterior density credible intervals have been calculated
under gamma-Dirichlet prior distribution by using the Markov chain Monte Carlo
technique. Convergence of Markov chain Monte Carlo samples is tested. In
addition, a Monte Carlo simulation is carried out to compare the effectiveness
of the proposed methods. Further, three different optimality criteria have been
taken into account to obtain the most effective censoring plans. Finally, a
real-life data set has been analyzed to illustrate the operability and
applicability of the proposed methods.
