A novel randomized scrambling technique for mean estimation of a finite population

Azeem, Muhammad; Asadullah,; Ijaz, Musarrat; Hussain, Sundus; Salahuddin, Najma; Salam, Abdul

doi:10.1016/j.heliyon.2024.e31690

Heliyon

2024

DOI: 10.1016/j.heliyon.2024.e31690

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A novel randomized scrambling technique for mean estimation of a finite population

Muhammad Azeem,

Asadullah,

Musarrat Ijaz

et al.

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2024

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Cited by 2 publications

References 23 publications

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Use of generalized randomized response model for enhancement of finite population variance: A simulation approach

Shabbir,

Movaheedi

2024

PLoS ONE

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Gupta et al. suggested an improved estimator by using the Diana and Perri model in estimating the finite population variance using the single auxiliary variable. On the same lines, Saleem et al. proposed a new scrambled randomized response model (RRT) based on two auxiliary variables for estimating the finite population variance. Recently Azeem et al. presented a new randomized response model in estimating the finite population variance. It is observed that Bias and MSE of these estimators up to first order of approximation seem to lack sufficient information. In this study, we rectify the bias and MSE expressions of the estimators proposed by Gupta et al., Saleem et al. and Azeem et al. Additionally, we suggest a new generalized class of estimators that is more efficient in comparison to the previously considered estimators. A simulation study is conducted to establish the behavior of the estimators. The suggested estimator performs better than the estimators considered by the authors earlier.

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Use of generalized randomized response model for enhancement of finite population variance: A simulation approach

Shabbir,

Movaheedi

2024

PLoS ONE

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An Improved Quantitative Optional Randomised Response Technique with Additive Scrambling using Two Questions Approach

Tiwari,

Pandey

2024

Communications in Advanced Mathematical Sciences

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In this paper, an improved two-stage and three-stage optional randomized response (ORR) models for quantitative variables that make the use of additive scrambling was proposed. These two-stage and three-stage models achieve efficient estimation of the mean and sensitivity level simultaneously in the single sample by using two questions. It is found that the proposed models perform better than the existing ORR models in terms of estimating sensitive attribute and sensitivity level simultaneously. It is found that the proposed three stage ORR model provides better estimates than the two-stage and one-stage ORR models and offers more privacy to the respondents with suitable choice of design parameters. The properties of the proposed models are demonstrated with the help of a numerical study.

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A novel randomized scrambling technique for mean estimation of a finite population

Cited by 2 publications

References 23 publications

Use of generalized randomized response model for enhancement of finite population variance: A simulation approach

Use of generalized randomized response model for enhancement of finite population variance: A simulation approach

An Improved Quantitative Optional Randomised Response Technique with Additive Scrambling using Two Questions Approach

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