Neutrosophic Sets and Systems
Abstract
In classical statistics, research typically relies on precise data to estimate the population mean, especially when auxiliary information is available. However, in the presence of outliers, conventional statistical approaches that depend on accurate data and auxiliary information encounter challenges. The primary objective is to attain the most accurate population mean estimates while minimizing the mean square error. Neutrosophic statistics, a more attractive framework than classical statistics, deals with data characterized by imprecision and uncertainty. In this current article, we adapt S¨ arndal’s strategy and introduce neutrosophic mean estimators, applying them to meteorological data, specifically stratified dew point data. In these proposed estimators, the incorporation of auxiliary information and the application of robust techniques address issues that arise due to outliers and imprecise observations. These factors can otherwise undermine the effectiveness of neutrosophic estimation methods. The article also suggests combining auxiliary information with extremely indeterminate neutrosophic observations, utilizing robust regression methods (Huber-M, Hampel-M, and Tukey-M), as well as the quantile regression technique. These approaches enhance the neutrosophic mean estimation process. The outcomes, which include the utilization of dew point data, showcase the superior performance of the proposed estimators compared to adapted estimators in a neutrosophic context. Ultimately, this study provides valuable insights by taking an initial step in defining and utilizing the concept of neutrosophic indeterminate extreme observations
Recommended Citation
Yadav, Vinay Kumar; Deepak Majhi; Alia A. Alkhathami; and Shakti Prasad. "Neutrosophic Mean Estimators Using Extreme Indeterminate Observations in Sample Surveys." Neutrosophic Sets and Systems 80, 1 (2025). https://digitalrepository.unm.edu/nss_journal/vol80/iss1/6
