ROBUST REGRESSION TYPE ESTIMATORS FOR BODY MASS INDEX UNDER EXTREME RANKED SET AND QUARTILE RANKED SET SAMPLING

Cetin, Arzu; KOYUNCU, NURSEL

doi:10.31801/cfsuasmas.1373759

ROBUST REGRESSION TYPE ESTIMATORS FOR BODY MASS INDEX UNDER EXTREME RANKED SET AND QUARTILE RANKED SET SAMPLING

COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS, cilt.73, sa.2, ss.336-348, 2024 (ESCI)

Yayın Türü: Makale / Tam Makale
Cilt numarası: 73 Sayı: 2
Basım Tarihi: 2024
Doi Numarası: 10.31801/cfsuasmas.1373759
Dergi Adı: COMMUNICATIONS FACULTY OF SCIENCES UNIVERSITY OF ANKARA-SERIES A1 MATHEMATICS AND STATISTICS
Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), TR DİZİN (ULAKBİM)
Sayfa Sayıları: ss.336-348
Hacettepe Üniversitesi Adresli: Evet

Özet

. Robust regression-type estimators of population mean that use auxiliary variable information are proposed by considering robust methods under extreme ranked set sampling (ERSS) and quartile ranked set sampling (QRSS). We have used the data concerning body mass index (BMI) for 800 people in Turkey in 2014. The real data example is applied to see efficiency of the estimators in ERSS and QRSS designs and it is found that the proposed estimators are better in these designs than the classical ranked set sampling (RSS) design. In addition, mean square error (MSE) and percent relative efficiency (PRE) are used to compare the performance of the adapted and proposed estimators.