An Improved Randomized Response Additive Model

Housila P Singh,Tanveer A Tarray

doi:10.4038/sljastats.v15i2.7412

Housila P Singh, Tanveer A Tarray

Open Access

https://doi.org/10.4038/sljastats.v15i2.7412

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Journal: Sri Lankan Journal of Applied Statistics	Publication Date: Sep 10, 2014
Citations: 10	License type: cc-by

Affiliation: Vikram University

Abstract

The Sri Lankan Journal of Applied Statistics(SLJAS) is an open-access, international, double-blind peer-reviewed journal published by the Institute of Applied Statistics, Sri Lanka (IASSL). The main purpose of the journal is to publish the results of original work on applications of Statistics and on theoretical and methodical aspects of Statistics. The journal also welcomes critical reviews including conceptual discussions, opinions and book reviews. Applications of Statistics in the area of Agriculture & Forestry, Medical, Dental and Veterinary Sciences, Natural, Physical Sciences, Social Sciences, Economics and Actuarial Science fall within the scope of the journal. This journal does not charge any fee for article processing and submission.

Highlights

Himmelfarb and Edgel (1980) have considered the usual additive model for gathering information on quantitative sensitive variables
We have suggested the new additive randomized response model utilizing the prior knowledge of mean ( ) and standard deviation ( ) of scrambling variable S
The proposed model is found to be more efficient both theoretically as well as numerically than the additive randomized response model studied by Gjestvang and Singh (2009) and the additive model due to Himmelfarb and Edgell‟s (1980)

Summary

Introduction

Himmelfarb and Edgel (1980) have considered the usual additive model for gathering information on quantitative sensitive variables. Gjestvang and Singh (2009) have mentioned that “the practical application of an additive model is much easier than the multiplicative model, that is respondents may like to add two numbers rather than doing painstaking work of multiplying two numbers or dividing two numbers; the improvement of the additive model has its importance in the literature.”. This led Gjestvang and Singh (2009) to suggest an alternative additive randomized response model. For real situations where such models can be used, the reader is referred to Eichhorn and Hayre (1983), Ahsanullah and Eichhorn (1988), Bar – Lev et al (2004) and Gjestvang and Singh (2009) etc

The Suggested Additive Model

Relative Efficiency With Respect to Different Additive Model

Conclusion