Improved regression in ratio type estimators based on robust M-estimation

Creative Commons License

Rather K. U. I., KOÇYİĞİT E. G., Onyango R., KADILAR C.

PLoS ONE, vol.17, no.12 December, 2022 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 17 Issue: 12 December
  • Publication Date: 2022
  • Doi Number: 10.1371/journal.pone.0278868
  • Journal Name: PLoS ONE
  • Journal Indexes: 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
  • Hacettepe University Affiliated: Yes


© 2022 Rather et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.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.