An efficient new scrambled response model for estimating sensitive population mean in successive sampling

Abstract

In this paper, a new scrambled randomized response (SRR) model has been proposed for estimating the population mean of a sensitive variable in presence of scrambled response under simple random sampling with replacement (SRSWR). The utility of proposed SRR model under two occasions successive sampling is also explored. It is found that the proposed SRR model is superior to the additive model of Gjestvang and Singh (2009) under SRSWR and successive sampling. We also proposed a composite class of estimators for estimating the population mean of sensitive variable under two occasions successive sampling. The proposed composite class of estimators under optimum conditions is shown to be more efficient than the classical ratio and exponential ratio type estimators respectively.

Keywords:

• Scrambled response
• Privacy protection
• Bias
• Mean squared error