Additive and subtractive scrambling in optional randomized response modeling

PLoS One. 2014 Jan 8;9(1):e83557. doi: 10.1371/journal.pone.0083557. eCollection 2014.

Abstract

This article considers unbiased estimation of mean, variance and sensitivity level of a sensitive variable via scrambled response modeling. In particular, we focus on estimation of the mean. The idea of using additive and subtractive scrambling has been suggested under a recent scrambled response model. Whether it is estimation of mean, variance or sensitivity level, the proposed scheme of estimation is shown relatively more efficient than that recent model. As far as the estimation of mean is concerned, the proposed estimators perform relatively better than the estimators based on recent additive scrambling models. Relative efficiency comparisons are also made in order to highlight the performance of proposed estimators under suggested scrambling technique.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Mathematics*
  • Models, Statistical*

Grants and funding

The work has been funded by the Institute of Scientific Research and Revival of Islamic Heritage at Umm Al-Qura University (grant # 43305030). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.