Improved Randomized Response Technique for Two Sensitive Attributes

Ewemooje, Olusegun Sunday; Amahia, G. N.

doi:10.16929/as/2015.639.78

Cited by 13 publications

(5 citation statements)

References 9 publications

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“…Research has also focused on estimating the proportions of two sensitive characteristics simultaneously. Ewemooje and Amahia (2015, 2016) [6,7] proposed new estimators for two related sensitive traits. Greenberg et al (1971) [9] adapted the RRT for quantitative responses.…”

Section: R1 R2mentioning

confidence: 99%

Randomized response technique in agricultural surveys

Banerjee,

Das

et al. 2024

Int. J. Stat. Appl. Math.

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Section: R1 R2mentioning

confidence: 99%

Randomized response technique in agricultural surveys

Banerjee,

Das

et al. 2024

Int. J. Stat. Appl. Math.

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“…Christofides [16] introduced the RR model to estimate the proportions of two dependent sensitive attributes at the same time. Studies on estimation of two dependent sensitive attributes have been reported by Lee et al [17]; Batool and Shabbir [18]; Ewemooje and Amahia [19]; Ewemooje [20]; and Ewemooje et al [21].…”

Section: Introductionmentioning

confidence: 99%

Partial Randomized Response Model for Simultaneous Estimation of Means of Two Sensitive Variables

Narjis

Shabbir

Onyango

2022

Mathematical Problems in Engineering

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In this study, a new partial randomized response model (RRM) has been proposed for estimating the population mean of two quantitative sensitive variables simultaneously. The utility of proposed model under stratification is also explored. The efficiency comparisons of the proposed model under simple and stratified random sampling are carried out numerically. A real data set was collected through direct questioning, proposed partial RRM and competitor randomized device from the students of statistics and animal sciences departments of Quaid-I-Azam University Islamabad, Pakistan. The performance of the proposed partial RRM is better than competitor RRM under simple and stratified random sampling.

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“…Fox (2016) named the Lee et al (2013) model as “double-decker model.” Christofides (2003) also developed a model for considering the problem of estimating proportions of two sensitive characteristics, and his model is similar to the simple model of the Lee et al (2013). Ewemooje and Amahia (2015) claim a new improved model by setting P = T in Lee et al (2013) model. Ewemooje, Amahia, and Adebola (2017) apply the improved claimed model by Ewemooje and Amahia (2015) to estimate the prevalence of induced abortion and multiple sexual partners.…”

Section: Introductionmentioning

confidence: 99%

“…Ewemooje and Amahia (2015) claim a new improved model by setting P ¼ T in Lee et al (2013) model. Ewemooje, Amahia, and Adebola (2017) apply the improved claimed model by Ewemooje and Amahia (2015) to estimate the prevalence of induced abortion and multiple sexual partners. Perri, Pelle, and Stranges (2016) have implemented the Lee et al (2013) model for estimating induced abortion and foreign irregular presence using the randomized response crossed model.…”

Section: Introductionmentioning

confidence: 99%

A Theory of Higher Order Interactions Between Sensitive Variables: Empirical Evidences and an Application to a Variety of Smoking

Olanipekun

Zhao

Wang

et al. 2021

Sociological Methods & Research

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In carrying out surveys involving sensitive characteristics, randomized response models have been considered among the best techniques since they provide the maximum privacy protection to the respondents and procure honest responses. Over the years, researchers have carried out studies on the estimation of proportions of the population possessing sensitive characteristics. However, there is a paucity of research studies that have addressed higher order interactions between these sensitive characters. In this article, we develop a new theory based on three proposed randomized response models which we name as: simple model, semi-crossed model, and fully crossed model. Twenty-one new unbiased estimators of seven parameters are introduced, their variance expressions are derived, and unbiased estimators of variances are developed. The three models are compared under various values of the parameters by computing the percent relative efficiency of one model over another model. The most efficient model is then applied to study the population proportions of three varieties of smoking habits among students, and their first- and second-order interactions. The last four sections (Ninth to Twelfth) are verifications of theoretical results using the Cramer–Rao lower bounds of variances for the developed 21 new estimators in randomized response sampling.

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Improved Randomized Response Technique for Two Sensitive Attributes

Cited by 13 publications

References 9 publications

Randomized response technique in agricultural surveys

Randomized response technique in agricultural surveys

Partial Randomized Response Model for Simultaneous Estimation of Means of Two Sensitive Variables

A Theory of Higher Order Interactions Between Sensitive Variables: Empirical Evidences and an Application to a Variety of Smoking

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