Improved Bar-Lev, Bobovitch, and Boukai Randomized Response Models

Odumade, Oluseun; Singh, Sarjinder

doi:10.1080/03610910802536736

Cited by 9 publications

(4 citation statements)

References 18 publications

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“…Several research workers, such as Greenberg et al [4], Moors [11], Folsom et al [25], Mangat [20], Singh and Horn [28], and Christofides [29], have contributed to the further development of the Warner [27] model. The interested readers may refer to Chaudhuri and Mukerjee [1], Tracy and Mangat [5], Perri [23], Yu et al [14], Hussain and Shabbir [31,32,33 and 34], Odumade and Singh [22] and the references listed therein.…”

Section: Introductionmentioning

confidence: 99%

On Bayesian Analysis of a General Class of Randomized Response Models In Social Surveys about Stigmatized Traits

Hussain¹,

Abid²,

Abbas³

2021

Journal of Data Science

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While conducting a social survey on stigmatized/sensitive traits, obtaining efficient (truthful) data is an intricate issue and estimates are generally biased in such surveys. To obtain trustworthy data and to reduce false response bias, a technique, known as randomized response technique, is now being used in many surveys. In this study, we performed a Bayesian analysis of a general class of randomized response models. Suitable simple Beta prior and mixture of Beta priors are used in a common prior structure to obtain the Bayes estimates for the proportion of a stigmatized/sensitive attributes in the population of interest. We also extended our proposal to stratified random sampling. The Bayes and the maximum likelihood estimators are compared. For further understanding of variability, we have also compared the prior and posterior distributions for different values of the design constants through graphs and credible intervals. The condition to develop a new randomized response model is also discussed.

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Section: Introductionmentioning

confidence: 99%

On Bayesian Analysis of a General Class of Randomized Response Models In Social Surveys about Stigmatized Traits

Hussain¹,

Abid²,

Abbas³

2021

Journal of Data Science

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“…To estimate μ y , many devices have been implemented in the literature [18]. Most of these use a coding mechanism of the response on Y , i.e.…”

Section: Saha's Model and Its Variantsmentioning

confidence: 99%

New scrambled response models for estimating the mean of a sensitive quantitative character

Diana

Perri

2010

Journal of Applied Statistics

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Moving from the scrambling mechanism recently suggested by Saha [25], three scrambled randomized response (SRR) models are introduced with the intent to realize a right trade-off between efficiency and privacy protection. The models perturb the true response on the sensitive variable by resorting to the multiplicative and additive approaches in different ways. Some analytical and numerical comparisons of efficiency are performed to set up the conditions under which improvements upon Saha’s model can be obtained and to quantify the efficiency gain. The use of auxiliary information is also discussed in a class of\ud estimators for the sensitive mean under a generic randomization scheme. The class includes also the three\ud proposed SRR models. Finally, some graphical comparisons are carried out from the double perspective\ud of the accuracy in the estimates and respondents’ privacy protection

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“…where k is assumed known constant [see Odumade and Singh (2009)] and S * = (S − θ) γ is the standardized scrambling variable. In other words, each respondent is requested to rotate a spinner unobserved by the interviewer, and if the spinner stops in the shaded area, then the respondent is requested to report the real response on the sensitive variable, say Yi; and if the spinner stops in the non shaded area, then the respondent is required to report the scrambled response, say Yi[(1 − k)S + KθS * 2 ].…”

Section: Figure 2 Spinner Of the Proposed Randomized Response Modelmentioning

confidence: 99%

An Improved Bar Lev, Bobovitch and Boukai Randomized Response Model Using Moments Ratios of Scrambling Variable

Singh¹,

Tarray²

2015

HJMS

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In this paper, we have suggested a new randomized response model and its properties have been studied. The proposed model is found to be more efficient than the randomized response models studied by Bar Lev et al. (2004) and Eichhorn and Hayre (1983). The relative efficiency of the proposed model has been studied with respect to the Bar Lev et al.s (2004) and Eichhorn and Hayres (1983) models. Numerical illustrations are also given to support the present study.

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Improved Bar-Lev, Bobovitch, and Boukai Randomized Response Models

Cited by 9 publications

References 18 publications

On Bayesian Analysis of a General Class of Randomized Response Models In Social Surveys about Stigmatized Traits

On Bayesian Analysis of a General Class of Randomized Response Models In Social Surveys about Stigmatized Traits

New scrambled response models for estimating the mean of a sensitive quantitative character

An Improved Bar Lev, Bobovitch and Boukai Randomized Response Model Using Moments Ratios of Scrambling Variable

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