An efficient new randomized response model

Singh, Housila P.; Gorey, Swarangi M.

doi:10.1080/03610926.2016.1213291

Cited by 2 publications

(2 citation statements)

References 4 publications

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“…Greenberg et al [3] extended the Warner's model by introducing unrelated innocuous attribute say, X as a replacement of A c in their RRT model. Some other developments in RRT are due to Chaudhuri and Mukerjee [4], Mahmood et al [5], Perri [6], Hussain and Shabbir [7], Lee et al [8], Abdelfatah and Mazloum [9], Tanveer and Singh [10; 11], Blair et al [12], Singh and Gorey [13], Bose [14] and Abid et al [15].…”

Section: Introductionmentioning

confidence: 99%

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

Narjis

Shabbir

2019

Scientia Iranica

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In this study, we propose optional randomized response technique (RRT) models in binary response situation. The utility of proposed optional RRT models under stratification are also explored. Gupta et al.[1] introduced an ingenious idea of optional RRT model, where question may be sensitive for one respondent but may not be sensitive for another. This study focus on estimating π, the prevalence of sensitive attribute, ω, the sensitivity level of the underlying sensitive question when the proportion of unrelated innocuous attribute π x is unknown. A new multi-question approach is proposed for estimation of parameters (π, ω). A comparison between proposed optional RRT models and corresponding full RRT models are carried out numerically under simple and stratified random sampling.

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Section: Introductionmentioning

confidence: 99%

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

Narjis

Shabbir

2019

Scientia Iranica

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“…It makes the interviewees less likely to give an imprecise answer. Several researches, such as Greenberg et al [2], Moors [3], Kim et al [4], Christo des [5], Hussain and Shabbir [6], Kim and Heo [7], Lee et al [8], Abdelfatah and Mazloum [9], Tanveer and Singh [10], Blair et al [11], and Singh and Gorey [12], contributed to further development of the RRT model. The interested readers may also refer to Chaudhuri and Mukerjee [13], Tracy and Mangat [14], Chaudhuri [15], Chaudhuri and Chirsto des [16], and the references listed therein.…”

Section: Introductionmentioning

confidence: 99%

Investigating the Impact of Simple and Mixture Priors on Estimating Sensitive Proportion Through a General Class of Randomized Response Models

et al. 2018

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Abstract. Randomized response is an e ective survey method to collect subtle information. It facilitates responding to over-sensitive issues and defensive questions (such as criminal behavior, gambling habits, drug addictions, abortions, etc.) while maintaining con dentiality. In this paper, we conducted a Bayesian analysis of a general class of randomized response models by using di erent prior distributions, such as Beta, Uniform, Je reys, and Haldane, under squared error loss, and precautionary and DeGroot loss functions. We have also expanded our proposal to the case of mixture of Beta priors under squared error loss function. The performance of the Bayes and maximum likelihood estimators has been evaluated in terms of mean squared errors. Moreover, an application with real dataset has been also provided to explain the proposal for practical considerations.

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An efficient new randomized response model

Cited by 2 publications

References 4 publications

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

Estimating Prevalence of Sensitive Attribute with Optional Unrelated Question Randomized Response Models under Simple and Stratified Random Sampling

Investigating the Impact of Simple and Mixture Priors on Estimating Sensitive Proportion Through a General Class of Randomized Response Models

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