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- MakaleAçık Erişim2026A robust calibration-based estimator in presence of non-sensitive and sensitive variables using moments in sample surveys
Yadav, Vinay Kumar; Bulut, Hasan; Zaman, Tolga
In survey sampling, the calibration approach is among the most widely used techniques to improve estimator efficiency. In this study, we introduced robust regression approaches into calibrated exponential ratio-type estimators for finite population mean, which helps to ensure better estimation precision when outliers are present in the data. In particular, using stratified random sampling, we introduce the first three moments of the origin of an auxiliary variable in the calibration approach. Second-and third-order moments are also considered by using exponential mean-type estimators. This technique is further modified for study involving sensitive variables, especially in research on personal relationships and intimate health where the respondent may under-report because of privacy concerns. Biased answers are often obtained when questions such as “Have you ever engaged in unprotected sex?" or “Have you ever been tested for a sexually transmitted infection?" By reducing these biases, our strong calibration estimators improve the accuracy of the study. In a simulation study, the data is randomly generated for each stratum and outliers are introduced at 5%, 10% and 25% by randomly selecting a specified proportion of the generated data and multiplying those values by a factor of 10. The findings of our suggested robust estimators perform better than those already in use, especially when it comes to managing sensitive data and outliers, which makes them more effective for real-world use.
- MakaleAçık Erişim2025Neutrosophic exponential estimators for estimating the population mean using auxiliary attributes: An application to temperature data
Zaman, Tolga; Sozen, Caglar
Classical statistical methods cannot directly incorporate measurement uncertainties and indeterministic elements into the model, leading to estimation errors and biases, especially in environmental datasets. Neutrosophic statistics, on the other hand, offers a broader approach that does not only rely on exact values, but also includes ambiguous, incomplete and conflicting information. In this study, we propose a new neutrosophic ratio-type estimator for estimating the population mean using monthly temperature data for Turkey for the period 2010-2022. This study proposes neutrosophic multivariate exponential estimators considering two auxiliary attributes. The mean square error equations of all proposed neutrosophic exponential estimators are obtained theoretically. Some conditions of neutrosophic multivariate exponential estimators are considered and the properties of these estimators are studied. The effectiveness of the proposed neutrosophic exponential estimators is analyzed using neutrosophic temperature data. In addition to real data analysis, extensive simulation studies are conducted under various scenarios to evaluate the performance of the estimators. The results demonstrate that the proposed estimators perform effectively under uncertainty and can be successfully applied in forecasting environmental variables.