Mean estimation of sensitive variables under measurement errors using optional RRT models

Abstract

In this study we propose an improved mean estimator for a sensitive variable under simple random sampling using Optional RRT models when measurement errors are present. We discuss two scrambling options and compare the mean square error (MSE) of the proposed estimator with some of the commonly used estimators. Both theoretical and empirical results show the superiority of the proposed estimator over existing estimators. Models are evaluated both with respect to efficiency and respondent privacy.