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Additive and Subtractive Scrambling in Optional Randomized Response Modeling

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  • Zawar Hussain
  • Mashail M Al-Sobhi
  • Bander Al-Zahrani

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.

Suggested Citation

  • Zawar Hussain & Mashail M Al-Sobhi & Bander Al-Zahrani, 2014. "Additive and Subtractive Scrambling in Optional Randomized Response Modeling," PLOS ONE, Public Library of Science, vol. 9(1), pages 1-11, January.
    • Handle: RePEc:plo:pone00:0083557
    DOI: 10.1371/journal.pone.0083557
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    References listed on IDEAS

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    1. Shaul K. Bar-Lev & Elizabeta Bobovitch & Benzion Boukai, 2004. "A note on randomized response models for quantitative data," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 60(3), pages 255-260, November.
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