Regression Estimators in Ranked Set, Median Ranked Set and Neoteric Ranked Set Sampling

Abstract

This paper proposes new regression estimators in median ranked set and neoteric ranked set sampling using one and two auxiliary variables. The proposed estimators have large gain in presicion compared to the classical ranked set sampling (RSS) design. A simulation study is designed to see the performance of suggested estimators. A real data set example is also used. In this data set, we have examined a rare endemic annual plant species which is grown in Turkey. We have found that suggested estimators are highly efficient that existing estimators.