Mean estimation based on FWA using ranked set sampling with single and multiple rankers
Conference paper > Full Text · 2016
Authors: Bekir Cetintav, Gözde ULUTAGAY, Selma Gurler, Neslihan Demirel
16th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, IPMU 2016, Germany, Eindhoven, International, Invitee, Full Text, 2016-2016, vol 611, pp 790-797
DOI: 10.1007/978-3-319-40581-0_64
Summary
The Ranked Set Sampling (RSS) is an advanced sampling method which improves the precision and accuracy of the mean estimator. In RSS, the units in the random sets which are drawn from a population are ranked by a ranking mechanism, and one of these ranked units is sampled from each set with a specific scheme. Ranking the units (visually or by a concomitant variable) could not be perfect because there is an uncertainty in decision making about the rank of a unit. In this study, we propose a fuzzy set perspective for RSS and an estimator for the population mean based on Fuzzy Weighted Average (FWA) operator. A real data application is given to illustrate the new approach for the single and multiple rankers.