A class of estimators in median ranked set sampling

Year 2019, Volume: 12 Issue: 2, 58 - 71, 30.12.2019

Nursel Koyuncu

Abstract

In this paper we have proposed a general class of estimators under median ranked set sampling (MRSS) both
one auxiliary and multi-auxiliary variables are known. The mean square error (MSE) and bias formulas are
derived by theoretically. We have conducted a simulation study to see the performance of proposed
estimators. Also it is shown that the proposed estimators are always more efficient than existing estimators by
theoretically. From simulation study we can say that suggested class of estimator performs better than AlOmari [1] estimator and conclude that regression type estimator gives always more efficient results than AlOmari [1] estimator.

Keywords

median ranked set sampling, ratio estimator, efficiency, Monte Carlo Simulation

Medyan sıralı küme örneklemesinde tahmin ediciler sınıfı

Abstract

Bu çalışmada, medyan sıralı küme örneklemesi (MSKÖ) altında hem bir
yardımcı değişken hem de birden çok yardımcı değişken bilinmesi durumunda genel
bir tahmin edici sınıfı önerilmiştir. Hata kareler ortalaması (HKO) ve yanlılık
formülleri teorik olarak elde edilmiştir. Önerilen tahmin edicilerin
performansını görmek için bir benzetim çalışması yapılmıştır. Ayrıca önerilen
tahmin edicilerin mevcut tahmin edicilerden her zaman daha etkin olduğu teorik
olarak gösterilmiştir. Benzetim çalışmasından, önerilen tahmin edici sınıfının
Al-Omari [1] tahmin edicisinden daha iyi performans gösterdiği söylenebilir ve
regresyon tipi tahmin edicinin, Al-Omari[1]  tahmin edicisinden her zaman daha etkin
sonuçlar verdiği sonucuna varılabilir

Keywords

Medyan sıralı küme örneklemesi, Oransal Tahmin, Etkinlik, Monte Carlo Benzetimi

References

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