Optimal ranked set sampling estimation based on medians from multiple set sizes

摘要

Ranked set sampling (RSS) is a sample selection technique that makes use of expert knowledge to rank sample units before measuring them. Even though rankings are not always perfect, RSS is useful in situations where obtaining measurements is costly, difficult, or destructive. Research in this area has tended to focus on the case where all set sizes are equal. This article represents a departure from that setting because we encounter different set sizes within a single sample. More specifically, we propose an alternative estimator for the median of a symmetric distribution using medians of ranked set samples of various set sizes from such a distribution. This estimator is seen to be robust over a wide class of symmetric distributions.