Some Log Type Estimators For Estimation of Population Mean of Sensitive Variable Using Additive Scrambling Model

Since it can be challenging to estimate the mean of a sensitive study changeable when direct methods of information collection are used to elicit sensitive information, through the use of the randomized response technique (RRT), protect respondents' privacy while also obtaining more valid and reliable information. The present study introduces a set of log-type estimators for calculating averages through the utilization of the additive scrambling model. The estimators' mean square error (MSE) is determined a maximum of two degrees. There are defined circumstances in which the estimators perform better. An investigation was carried out utilizing three datasets, and the findings indicated that the suggested estimators outperform estimators found in the previous research both in efficiency comparison and empirical investigations. The first table shows the Mean Square Errors (MSEs) of the proposed and existing estimators used in this research, and we found out that Zp7 has the smallest MSE, followed by the Zp1, Zp2 , Zp3 , Zp 4 and co. The second table show the same results where Zp7 is having highest Percentage Relative Efficiecy (PRE), then other estimators follows. This explain that it is the most efficient estimator in this research.