Efficient estimation of population variance of a sensitive variable using a new scrambling response model

This study introduces a pioneering scrambling response model tailored for handling sensitive variables. Subsequently, a generalized estimator for variance estimation, relying on two auxiliary information sources, is developed following this novel model. Analytical expressions for bias, mean square e...

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Published in:Scientific reports 2023-11, Vol.13 (1), p.19913-19913, Article 19913
Main Authors: Saleem, Iram, Sanaullah, Aamir, Al-Essa, Laila A., Bashir, Shakila, Al Mutairi, Aned
Format: Article
Language:English
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639/705/1041
639/705/1042
639/705/1046
639/705/531
Approximation
Bias
Humanities and Social Sciences
Mean square errors
multidisciplinary
Privacy
Science
Science (multidisciplinary)
Simulation
Variables
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description This study introduces a pioneering scrambling response model tailored for handling sensitive variables. Subsequently, a generalized estimator for variance estimation, relying on two auxiliary information sources, is developed following this novel model. Analytical expressions for bias, mean square error, and minimum mean square error are meticulously derived up to the first order of approximation, shedding light on the estimator’s statistical performance. Comprehensive simulation experiments and empirical analysis unveil compelling results. The proposed generalized estimator, operating under both scrambling response models, consistently exhibits minimal mean square error, surpassing existing estimation techniques. Furthermore, this study evaluates the level of privacy protection afforded to respondents using this model, employing a robust framework of simulations and empirical studies.
doi_str_mv 10.1038/s41598-023-45427-2
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subjects 639/705/1041
639/705/1042
639/705/1046
639/705/531
Approximation
Bias
Humanities and Social Sciences
Mean square errors
multidisciplinary
Privacy
Science
Science (multidisciplinary)
Simulation
Variables
title Efficient estimation of population variance of a sensitive variable using a new scrambling response model
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