A Study of Gjestvang and Singh Randomized Response Model Using Ranked Set Sampling

2022 ◽  
pp. 86-103
Author(s):  
Shravya Jasti ◽  
Stephen A. Sedory ◽  
Sarjinder Singh
Keyword(s):  
Simulation Study ◽  
Relative Efficiency ◽  
Secondary Data ◽  
Ranked Set Sampling ◽  
Response Model ◽  
Randomized Response ◽  
Proposed Model ◽  
The Mean ◽  
Sensitive Variable ◽  
Randomized Response Model

In this chapter, the authors investigate the performance of the Gjestvang and Singh randomized response model for estimating the mean of a sensitive variable using ranked set sampling along the lines of Bouza. The proposed estimator is found to be unbiased, and a variance expression is derived. Then a simulation study is carried out to judge the magnitude of relative efficiency in various situations. At the end, the proposed model is assessed based on real secondary data applications. A set of SAS codes is also included.

An improved scrambled model for estimating the mean of a sensitive character

2021 ◽  
Vol 16 (2) ◽  
pp. 87-95
Author(s):  
Housila P. Singh ◽  
Preeti Patidar
Keyword(s):  
Tax Evasion ◽  
Response Model ◽  
Randomized Response ◽  
Drug Usage ◽  
Induced Abortions ◽  
Sensitive Character ◽  
The Mean ◽  
Sensitive Variable ◽  
Randomized Response Model

This paper suggests a new randomized response model useful for gathering information on quantitative sensitive variable such as drug usage, tax evasion and induced abortions etc. The resultant estimator has been found to more efficient than the estimator of the Saha (2007) under some realistic conditions. We have illustrated results numerically.

scholarly journals A Remark on Gupta, Gupta and Singh Optional Randomized Response Model

2019 ◽  
Vol 15 (1) ◽  
pp. 43-73
Author(s):  
H. P. Singh ◽  
S. M. Gorey
Keyword(s):  
Response Model ◽  
Randomized Response ◽  
Numerical Illustration ◽  
The Mean ◽  
Randomized Response Model

Abstract Gupta et al (2002) suggested an optional randomized response model under the assumption that the mean of the scrambling variable S is ‘unity’ [i.e. µs = 1]. This assumption limits the use of Gupta et al’s (2002) randomized response model. Keeping this in view we have suggested a modified optional randomized response model which can be used in practice without any supposition and restriction over the mean (µs) of the scrambling variables S. It has been shown that the estimator of the mean of the stigmatized variable based on the proposed optional randomized response sampling is more efficient than the Eicchorn and Hayre (1983) procedure and Gupta et al’s (2002) optional randomized technique when the mean of the scrambling S is larger than unity [i.e. µs > 1]. A numerical illustration is given in support of the present study.

scholarly journals A Proficient Two-Stage Stratified Randomized Response Strategy

2018 ◽  
Vol 17 (1) ◽  
Author(s):  
Tanveer A. Tarray ◽  
Housila P. Singh
Keyword(s):  
Optimal Allocation ◽  
Response Strategy ◽  
Response Model ◽  
Randomized Response ◽  
Two Stage ◽  
Model Based ◽  
Large Gain ◽  
Proposed Model ◽  
Randomized Response Model

A stratified randomized response model based on R. Singh, Singh, Mangat, and Tracy (1995) improved two-stage randomized response strategy is proposed. It has an optimal allocation and large gain in precision. Conditions are obtained under which the proposed model is more efficient than R. Singh et al. (1995) and H. P. Singh and Tarray (2015) models. Numerical illustrations are also given in support of the present study.

scholarly journals A Modified Forced Randomized Response Model

2020 ◽  
pp. 36-50
Author(s):  
Adefemi Adeniran ◽  
A. A. Sodipo ◽  
C. G. Udomboso
Keyword(s):  
Random Sampling ◽  
Simple Random Sampling ◽  
Sampling Techniques ◽  
Response Model ◽  
Randomized Response ◽  
Numerical Illustration ◽  
Proposed Model ◽  
Efficiency Comparison ◽  
Randomized Response Model ◽  
Sensitive Group

In this paper, we proposed a new Randomized Response Model (RRM) that estimate proportion of people in a population (P) belonging to a sensitive group (S) under study. Simple random sampling with replacement and stratified simple random sampling scheme were adopted. Maximum likelihood and Bayesian estimation procedures of the proposed model were developed and compared. The sampling distribution (expectation and variance) of the proposed estimator under the two sampling techniques, efficiency comparison of the proposed model with some existing models, and numerical illustration of all the compared models were also explored. We found that the proposed model outperformed other existing RRMs in terms of efficiency and it proved to be more protective in designing survey for sensitive related issues.

scholarly journals Variance Estimation Procedure Using Scrambled Responses and Multi-Auxiliary Variables In Multi-Phase Sampling

2021 ◽  
Author(s):  
Nadia Mushtaq
Keyword(s):  
Simulation Study ◽  
Variance Estimation ◽  
Estimation Procedure ◽  
Randomized Response ◽  
Auxiliary Variables ◽  
Exponential Regression ◽  
Proposed Model ◽  
Phase Sampling ◽  
Multi Phase ◽  
Sensitive Variable

Variations in the population can be estimated by variance estimation. In this study, we consider variance estimation procedure using scrambled randomized response for sensitive variable using multi-auxiliary variables in multi-phase sampling. Under Noor-ul-Amin et al. (2018) RRT model, generalized exponential regression type estimator for case-1and case-2 are derived. A simulation study is presented to illustrate the application and computational details. It is observed that proposed model showed better results under both cases.

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