An alternative to stratified Kim and Warde's randomized response model using optimal (Neyman) allocation

2014 ◽  
Vol 9 (1) ◽  
pp. 37-62 ◽  
Author(s):  
Housila P. Singh ◽  
Tanveer A. Tarray
Keyword(s):  
Response Model ◽  
Randomized Response ◽  
Neyman Allocation ◽  
Randomized Response Model

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 An Additive Stratified Quantitative Attribute Randomized Response Model

2014 ◽  
Vol 27 (2) ◽  
pp. 239-247
Author(s):  
Gi-Sung Lee ◽  
Seung-Chul Ahn ◽  
Ki-Hak Hong ◽  
Chang-Kyoon Son
Keyword(s):  
Response Model ◽  
Randomized Response ◽  
Randomized Response Model

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.

Sign in / Sign up

Export Citation Format

Share Document