Sharif University of Technology Scientia Iranica 1026-3098 32 8 2025 03 01 A new class of robust ratio estimators for finite population variance 24033 10.24200/sci.2022.57175.5100 EN Tolga Zaman Department of Mathematical Engineering, Faculty of Engineering and Natural Science, Gumushane University, Gumushane 29100, Turkey. Hasan Bulut Department of Statistics, Faculty of Science, Ondokuz Mayıs University, 55139 Samsun, Turkey. Journal Article 2020 11 15 It is a general practice to use robust estimates to improve ratio estimators using functions of the parameters of an auxiliary variable. In this study, a new class of robust estimators based upon the Minimum Covariance Determinant (MCD) and the Minimum Volume Ellipsoid (MVE) robust covariance estimates have been suggested for estimating population variance in the presence of outlier values in the data set for the simple random sampling. The expression for the Mean Square Error (MSE) of the proposed class of estimators is derived from the first degree of approximation. The efficiency of the proposed class of robust estimators is compared with some competing estimators discussed in the literature, and found that proposed estimators are better than other mentioned estimators here. In addition, real data set and simulation studies are performed to present the efficiencies of the estimators. We demonstrate theoretically and numerically that the proposed class of estimators performs better than all other competitor estimators under all situations. https://scientiairanica.sharif.edu/article_24033_ebf1c7d3fd22600366c64fb77ee97a73.pdf