An Improved Two-stage Stratified Randomized Response Model for Estimating Sensitive Proportion

2019 ◽  
pp. 004912411987596
Author(s):  
Zawar Hussain ◽  
Salman Arif Cheema ◽  
Ishtiaq Hussain
Keyword(s):  
Random Sampling ◽  
Numerical Study ◽  
Stratified Sampling ◽  
Stratified Random Sampling ◽  
Response Model ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Suggested Model ◽  
Two Stage ◽  
Randomized Response Model

This article is about making correction in Tarray, Singh, and Zaizai model and further improving it when stratified random sampling is necessary. This is done by using optional randomized response technique in stratified sampling using a combination of Mangat and Singh, Mangat, and Greenberg et al. models. The suggested model has been studied assuming proportional and Neyman allocation schemes. Numerical results show larger gains in efficiency. Through a detailed numerical study, it is established that the suggested model is relatively more efficient than the Kim and Warde model and the models mentioned above.

An Improved Two-stage Randomized Response Model for Estimating the Proportion of Sensitive Attribute

2021 ◽  
pp. 004912412110099
Author(s):  
Ghulam Narjis ◽  
Javid Shabbir
Keyword(s):  
Random Sampling ◽  
Stratified Random Sampling ◽  
Response Model ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Two Stage ◽  
Sensitive Attribute ◽  
Mean And Variance ◽  
Efficiency Comparison ◽  
Randomized Response Model

The randomized response technique (RRT) is an effective method designed to obtain the stigmatized information from respondents while assuring the privacy. In this study, we propose a new two-stage RRT model to estimate the prevalence of sensitive attribute ([Formula: see text]). A simulation study shows that the empirical mean and variance of proposed estimator are close to corresponding theoretical values. The utility of proposed two-stage RRT model under stratification is also explored. An efficiency comparison between proposed two-stage RRT model and some existing RRT models is carried out numerically under simple and stratified random sampling.

A Dexterous Optional Randomized Response Model

2015 ◽  
Vol 46 (3) ◽  
pp. 565-585 ◽  
Author(s):  
Tanveer A. Tarray ◽  
Housila P. Singh ◽  
Zaizai Yan
Keyword(s):  
Stratified Sampling ◽  
Response Model ◽  
Randomized Response Technique ◽  
Randomized Response ◽  
Suggested Model ◽  
Neyman Allocation ◽  
Randomized Response Model ◽  
Sensitive Group

This article addresses the problem of estimating the proportion π S of the population belonging to a sensitive group using optional randomized response technique in stratified sampling based on Mangat model that has proportional and Neyman allocation and larger gain in efficiency. Numerically, it is found that the suggested model is more efficient than Kim and Warde stratified randomized response model and Mangat model.

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.

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