Improved regression in ratio type estimators based on robust M-estimation
PLOS ONE, sa.-, ss.1-22, 2022 (SCI-Expanded, Scopus)
- Yayın Türü: Makale / Tam Makale
- Basım Tarihi: 2022
- Doi Numarası: 10.1371/journal.pone.0278868
- Dergi Adı: PLOS ONE
- Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Agricultural & Environmental Science Database, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, Biotechnology Research Abstracts, Chemical Abstracts Core, EMBASE, Food Science & Technology Abstracts, Index Islamicus, Linguistic Bibliography, MEDLINE, Pollution Abstracts, Psycinfo, zbMATH, Directory of Open Access Journals
- Sayfa Sayıları: ss.1-22
- Dokuz Eylül Üniversitesi Adresli: Evet
Özet
In this article,
a new robust ratio type estimator using the Uk’s redescending M-estimator is
proposed for the estimation of the finite population mean in the simple random
sampling (SRS) when there are outliers in the dataset. The mean square error
(MSE) equation of the proposed estimator is obtained using the first order of
approximation and it has been compared with the traditional ratio-type
estimators in the literature, robust regression estimators, and other existing
redescending M-estimators. A real-life data and simulation study are used to
justify the efficiency of the proposed estimators. It has been shown that the
proposed estimator is more efficient than other estimators in the literature on
both simulation and real data studies.