Asymptotically optimal progressive censoring plans based on Fisher information

Considering the Fisher information about a single parameter contained in a progressively Type-II censored sample, the problem of optimal progressive censoring plans arises. By introducing the notion of asymptotically optimal designs, we show that right censoring is ‘almost’ optimal for many important distributions including scale families of extreme value, normal, logistic, and Laplace distributions. Moreover, it turns out that for the extreme value distribution right censoring is the only asymptotically optimal one-step censoring scheme.