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.

Handling non-response under ranked set sampling by Substitution method and its application to COVID-19 data

“…Mehta (2016) proposed the ratio-cum-product estimator for the estimation of a finite population mean. Koyuncu (2018) has proposed regression type estimators under median ranked set sampling and neoteric ranked set sampling since they provide more efficient results than classical RSS. Khan and Shabbir (2019) proposed RSS estimators by using the transformed auxiliary variable.…”

Medyan Sıralı Küme Örneklemesinde Normal Dağılımın Konum Parametresi İçin Shrinkage Tahmin Edicileri

“…(2010), simetrik dağılımlarda yığın ortalamasını tahmin etmek için MSKÖ kullanarak shrinkage ve önsel test işlemine dayalı yeni bir tahmin metodu önermişlerdir[16] Koyuncu (2018),. bir ve iki yardımcı değişken kullanarak MSKÖ ve neoterik SKÖ'de yeni regresyon tahmin edicileri önermiştir[17]. Önerilen tahmin edicilerin SKÖ'ye göre daha etkin oldukları belirlenmiştir.…”