A robust calibration-based estimator in presence of non-sensitive and sensitive variables using moments in sample surveys

OpenAlex English

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.