The New Sub-Regression Type Estimator in Ranked Set Sampling
JOURNAL OF STATISTICAL THEORY AND PRACTICE, cilt.0, ss.17-27, 2023 (ESCI, Scopus)
- Yayın Türü: Makale / Tam Makale
- Cilt numarası: 0
- Basım Tarihi: 2023
- Doi Numarası: 10.1007/s42519-023-00324-9
- Dergi Adı: JOURNAL OF STATISTICAL THEORY AND PRACTICE
- Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, zbMATH
- Sayfa Sayıları: ss.17-27
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
In this study, a new sub-regression type
estimator for ranked set sampling (RSS) is proposed based on the idea of a
sub-ratio estimator given in Koçyiğit and Kadılar (2022). The proposed unbiased
estimator's mean square error (MSE) is obtained and compared theoretically with
other estimators. The theoretical results have been supported by the different
simulations and real-life data sets studies and have shown that the proposed
estimator is more effective than the estimators in the literature. It is also
seen that the number of repetitions in the RSS affected the effectiveness of
the sub-estimators.