Optimum Plans for Progressive Censored Competing Risk Data Under Kies Distribution

This paper considers optimal inference for the competing risks model when the latent failure times follow the two-parameter Kies distribution with a common shape parameter. We obtain different optimum schemes for competing risks model under progressive type-II censoring scheme. The existence and uniqueness properties of maximum likelihood estimates of parameters are derived. Further observed and expected Fisher information matrices are evaluated. In sequel approximate intervals of Kies parameters are computed. A simulation study has been used to evaluate proposed estimators. Analysis of a real data set is presented as well, for illustration purpose. Furthermore, we obtain optimal censoring plans by minimizing the experimental cost and variance associated with the estimators by considering single as well as multi-objective frameworks.