Forced quantitative randomized response model: a new device

Gjestvang, Christopher R.; Singh, Sarjinder

doi:10.1007/s00184-006-0108-1

Cited by 33 publications

(11 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This is a reason why only the multiplicative randomized response models accredited to Bar-Lev et al (2004) and Eichhorn and Hayre (1983) have been considered here and seem more practical than the other randomized response models. No doubt the forced quantitative randomized response model due to Gjestvang and Singh (2007), and other additive models in Chaudhuri and Roy (1997) and Chaudhuri and Mukerjee (1988) are another possibilities, but those models may also lead to negative estimates of total. We are not ignoring the possibility that those models can also be used by making a careful choice of a scrambling variable which would ensure that none of the transformed response will take a negative value as also pointed out by one of the reviewers.…”

Section: Proposed Estimatormentioning

confidence: 95%

A pseudo-empirical log-likelihood estimator using scrambled responses

Singh

Kim

2011

Statistics & Probability Letters

Self Cite

View full text Add to dashboard Cite

Section: Proposed Estimatormentioning

confidence: 95%

A pseudo-empirical log-likelihood estimator using scrambled responses

Singh

Kim

2011

Statistics & Probability Letters

Self Cite

View full text Add to dashboard Cite

“…Therefore, we define a general class of estimators for μ y which allows for one auxiliary variable, coded or not, and which includes as special cases the estimators (1), (3), (8) and (13).…”

Section: Auxiliary Information In Srr Modelsmentioning

confidence: 99%

“…Comparisons will be now performed mainly to investigate the efficiency of the estimators (3), (8) and (13) upon estimator (1). We preliminarily consider the behavior of V(μ EH ), V(μ PB ) and…”

Section: Multiplicative and Additive Models Efficiencymentioning

confidence: 99%

See 1 more Smart Citation

New scrambled response models for estimating the mean of a sensitive quantitative character

Diana

Perri

2010

Journal of Applied Statistics

View full text Add to dashboard Cite

Moving from the scrambling mechanism recently suggested by Saha [25], three scrambled randomized response (SRR) models are introduced with the intent to realize a right trade-off between efficiency and privacy protection. The models perturb the true response on the sensitive variable by resorting to the multiplicative and additive approaches in different ways. Some analytical and numerical comparisons of efficiency are performed to set up the conditions under which improvements upon Saha’s model can be obtained and to quantify the efficiency gain. The use of auxiliary information is also discussed in a class of\ud estimators for the sensitive mean under a generic randomization scheme. The class includes also the three\ud proposed SRR models. Finally, some graphical comparisons are carried out from the double perspective\ud of the accuracy in the estimates and respondents’ privacy protection

show abstract

“…This is an RR design for a binary variable. A recent example of a forced quantitative RR design is presented in Gjestvang and Singh (2007).…”

Section: Introductionmentioning

confidence: 99%

The randomized response log linear model as a composite link model

Hout

Gilchrist

Heijden

2010

Statistical Modelling

View full text Add to dashboard Cite

In randomized response (RR) designs, misclassification is used to protect the privacy of respondents when sensitive questions are asked. A generalized linear model with a composite link function is presented to formulate log linear models that take the RR design into account. The approach is extended to model the situation where some respondents do not follow the instructions of the RR design. For example, if there are three binary RR variables with regard to practicing fraud, the 2 × 2 × 2 cross-classification of the true answers is latent due to the misclassification. Using composite link functions, log linear models can be specified for the latent table to investigate possible association between the variables. Fast iteratively re-weighted least squares algorithms are presented.

show abstract

Forced quantitative randomized response model: a new device

Abstract: Sensitive variable, Randomized response model, Forced questions,

Cited by 33 publications

References 17 publications

A pseudo-empirical log-likelihood estimator using scrambled responses

A pseudo-empirical log-likelihood estimator using scrambled responses

New scrambled response models for estimating the mean of a sensitive quantitative character

The randomized response log linear model as a composite link model

Contact Info

Product

Resources

About