Mean Estimators for Sensitive Variables Using Scrambled Response Models

Abstract

The most popular methods for sensitive study are regression estimation methods that use standard regression coefficients. When it comes to survey researchers, mean estimation is an important issue. The vast majority of population mean estimation methods that can be found in sampling theory are meant only to be utilized with non-sensitive data-sets. When the variable that is important is sensitive, such as drug usage, illegal income, abortion, exam cheating, the amount of income tax due, employee rule-breaking, etc., these estimation methods cannot operate efficiently. In this article, we propose a novel method for computing the population mean using exponential technique of robust-type regression estimators for scrambled response model (SRM) under simple random sampling (SRS). The mean square error (MSE) equation is generated using a first-order approximation and examined with existing estimating techniques in order to evaluate the efficacy of the new approach. Additionally, the proposed estimator's percentage relative efficiency (PRE) is determined compared to other estimators. The effectiveness of the proposed method is demonstrated using real data sets. According to the results, the suggested estimator performs better than other estimators in the literature.