Optimal Progressive Type-II Censoring Schemes for Nonparametric Confidence Intervals of Quantiles

In this article, optimal progressive censoring schemes are examined for the nonparametric confidence intervals of population quantiles. The results obtained can be universally applied to any continuous probability distribution. By using the interval mass as an optimality criterion, the optimization process is free of the actual observed values from the sample and needs only the initial sample size n and the number of complete failures m. Using several sample sizes combined with various degrees of censoring, the results of the optimization are presented here for the population median at selected levels of confidence (99, 95, and 90%). With the optimality criterion under consideration, the efficiencies of the worst progressive Type-II censoring scheme and ordinary Type-II censoring scheme are also examined in comparison to the best censoring scheme obtained for fixed n and m.