A Three-Stage Optional Randomized Response Model

Mehta, Samridhi; Dass, Bal Kishan; Shabbir, Javid; Gupta, Sat

doi:10.1080/15598608.2012.695558

Cited by 31 publications

(56 citation statements)

References 8 publications

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“…We take scrambling variables the same as defined in Mehta et al [14] ORRM. To report the first (second) response, respondents are requested to use additive (subtractive) scrambling with the variable .…”

Section: Proposed Proceduresmentioning

confidence: 99%

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

2014

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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.

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Section: Proposed Proceduresmentioning

confidence: 99%

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

2014

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“…Randomized response technique (RRT) is an important strategy to prevent or reduce SDB and is widely used in survey interviews. The rst RRT model was proposed by Warner in 1965 [27], it was modied later by many researchers including Greenberg et al (1969) [9], Eichhorn (1983) [6], Gupta et al (2002) [11], Gupta et al (2010) [13], Sousa et al (2010) [24], and Mehta et al (2012) [18] etc. It…”

Section: Randomized Response Technique (Rrt) Modelsmentioning

confidence: 99%

Ratio estimation of the mean under RRT models

Zhang

2019

Journal of Statistics and Management Systems

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Ratio estimation is a parameter estimation technique that uses a known auxiliary variable that is correlated with the study variable. In many situations, the primary variable of interest may be sensitive and it cannot be observed directly. However, we can observe directly a non-sensitive variable that is highly correlated with the study variable. In these cases, we have to rely on some Randomized Response Technique (RRT) models to obtain information on the study variable.In this thesis, we rst review some RRT models, some general ratio and product estimation techniques, and two Kalucha et al. (2015) ratio estimators that are based on Gupta et al. (2010) additive optional RRT model. One of the Kalucha et al. (2015) estimators, the multiplicative ratio estimator, did not work eciently and was abandoned. The main focus of this thesis is on xing the Kalucha et al. (2015)abandoned multiplicative ratio estimator and reevaluating its performance. We discuss the Bias and the Mean Square Error (MSE) of our proposed multiplicative ratio estimator correct up to rst order approximation, and present the comparisons with other estimators under the additive optional RRT model. A simulation study is also conducted to verify the theoretical result. Both the theoretical and the empirical results show that the proposed multiplicative ratio estimator is more ecient than the ordinary RRT estimator that does not utilize the auxiliary variable. It also compares well with the additive ratio estimator of Kalucha et al. (2015).

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“…In Gupta et al [14], a large value of truth parameter (T) is needed, if the question is highly sensitive. Mehta et al [1] present two-and three-stage models by introducing a forced scrambling parameter (F). Their models perform better in estimating mean but they did not discuss the performance of sensitivity estimator.…”

Section: Introductionmentioning

confidence: 99%

“…Their models perform better in estimating mean but they did not discuss the performance of sensitivity estimator. Motivated by Mehta et al [1] we propose two ORRMs which are more efficient than the [1], two-and three-stage models. Before presenting the proposed models in the next section, we briefly discuss the [1] two-and three-stage procedures.…”

Section: Introductionmentioning

confidence: 99%

Improved Randomized Response in Optional Scrambling Models

Hussain¹,

Shahid²

2019

JSTA

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In the present study, we discuss the issue of increasing the respondents cooperation in sensitive surveys. When the question is highly sensitive then the cooperation from the respondents is decreased. We propose two optional randomized response models (ORRMs) to increase the respondents cooperation. A comparison of proposed ORRMs with [1] two-and three-stage models have been made. It is found that, in estimating mean and sensitivity level, the proposed strategies perform better than [1] twoand three-stage models. A comparison is also made among the [1] two-and three-stage models and proposed ORRMs to identify the best one. Numerical illustration are also given in favor of the algebraic results.

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A Three-Stage Optional Randomized Response Model

Cited by 31 publications

References 8 publications

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

Additive and Subtractive Scrambling in Optional Randomized Response Modeling

Ratio estimation of the mean under RRT models

Improved Randomized Response in Optional Scrambling Models

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