A class of estimators in median ranked set sampling

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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