Quantile Estimation from Ranked Set Sampling Data

We consider estimation of quantiles when data are generated from ranked set sampling. A new estimator is proposed and is shown to have a smaller asymptotic variance for all distributions. It is also shown that the optimal sampling strategy is to select observations with one fixed rank from different ranked sets. Both the optimal rank and the relative efficiency gain with respect to simple random sampling are distribution-free and depend on the set size and the given probability only. In the case of median estimation, it is analytically shown that the optimal design is to select the median from each ranked set. AMS (2000) subject classification. 62G32, 62G99.