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This article proposes two – stage stratified partial randomized response models based on Tracy and Osahan (1995) model and have large gain in precision. It is also shown that the proposed models are more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in the case of completely truthful reporting and less than completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper, we have 89 suggested an improved randomized response (RR) model for estimating π, the proportion of respondents in the population belonging to the sensitive group. We have studied the properties of the RR model. The proposed model is found to be more efficient than the RR model studied by Gjestvang and Singh (2006). Numerical illustration is given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population proportion π of a sensitive group. We have suggested a randomized response (RR) model that is more efficient than the one envisaged by Gjestvang and Singh ([2006), A new randomized response model, Journal of Royal Statistcal Socity, B, (3), 523–530]. [ABSTRACT FROM AUTHOR]

Eichhorn & Hayre (1983) considered a randomized response procedure suitable for estimating the mean response, when the sensitive variable under investigation is quantitative in nature. They have obtained an estimate for the mean of the quantitative response variable under investigation and studied its properties. Bar-Lev et al. (2004) have suggested an alternative procedure, which use a design parameter (controlled by the experimenter) that generalizes Eichhorn & Hayre’s (1983) results. They have also proved that the estimator proposed by them has uniformly smaller variance as compared to that of Eichhorn & Hayre (1983) in certain condition. In this paper we have suggested a simple procedure of improving the Eichhorn & Hayre (1983) and Bar-Lev et al. (2004) models along with its properties. It has been shown that the proposed procedure is uniformly better than Bar-Lev et al. (2004) procedure. The proposed procedure is also uniformly better than Eichhorn and Hayre’s (1983) procedure under the same condition in which the Bar-Lev et al.'s (2004) procedure is more efficient than Eichhorn & Hayre’s (1983) procedure. Numerical illustration is given in support of the present study [ABSTRACT FROM AUTHOR]

This article addresses the problem of estimating the proportion πS 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. [ABSTRACT FROM AUTHOR]

This paper suggests a new stratified randomized response model based on Kuk's [Biometrika (1990), 77, 2, pp.436–438] model that has Neyman allocation and considerable gain in precision. It has been identified that the stratified randomized response models due to Kim and Warde (2004), Kim and Elam's (2005), and Kim and Elam's (2007) are members of the proposed model. It is shown that the proposed model is more efficient than Kuk's (1990) model both theoretically and empirically. The results of this paper are also extended in the situation when trials are repeated. [ABSTRACT FROM AUTHOR]

The crux of this article is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Mangat (1991) randomization device by utilizing the Poisson distribution in simple random sampling and stratified sampling. Properties of the proposed randomized response (RR) model have been studied along with recommendations. It is also shown that the proposed model is more efficient than that of Land et al. (2011) in simple random sampling and that of Lee et al. (2013) in stratified random sampling when the proportion of persons possessing a rare unrelated attribute is known. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

Taking clue from the pioneer work of Singh and Sedory (2013), we have suggested a new stratified randomized response (RR) model. The properties of the suggested stratified RR model have been studied under proportional and “Neyman” allocations. The study has been also carried out in the presence of crude prior estimates. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper aimed at providing an efficient new unbiased estimator for estimating the proportion of a potentially sensitive attribute in survey sampling. The suggested randomization device makes use of the means, variances of scrambling variables, and the two scalars lie between “zero” and “one.” Thus, the same amount of information has been used at the estimation stage. The variance formula of the suggested estimator has been obtained. We have compared the proposed unbiased estimator with that of Kuk (1990) and Franklin (1989), and Singh and Chen (2009) estimators. Relevant conditions are obtained in which the proposed estimator is more efficient than Kuk (1990) and Franklin (1989) and Singh and Chen (2009) estimators. The optimum estimator (OE) in the proposed class of estimators has been identified which finally depends on moments ratios of the scrambling variables. The variance of the optimum estimator has been obtained and compared with that of the Kuk (1990) and Franklin (1989) estimator and Singh and Chen (2009) estimator. It is interesting to mention that the “optimum estimator” of the class of estimators due to Singh and Chen (2009) depends on the parameter π under investigation which limits the use of Singh and Chen (2009) OE in practice while the proposed OE in this paper is free from such a constraint. The proposed OE depends only on the moments ratios of scrambling variables. This is an advantage over the Singh and Chen (2009) estimator. Numerical illustrations are given in the support of the present study when the scrambling variables follow normal distribution. Theoretical and empirical results are very sound and quite illuminating in the favor of the present study. [ABSTRACT FROM PUBLISHER]

The crux of this paper is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Mangat (1992) randomization device by utilizing the Poisson distribution in survey sampling. It is shown that the proposed model is more efficient than Land et al. (2011) when the proportion of persons possessing a rare unrelated attribute is known. Properties of the proposed randomized response model have been studied along with recommendations. We have also extended the proposed model to stratified random sampling on the lines of Lee et al. (2013). It has been also shown that the proposed estimator is better than Lee et al.'s (2013) estimator. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM PUBLISHER]

This paper applies stratified random sampling using Neyman allocation to Mangat et al. (1992) unrelated question randomized response (RR) strategy for both completely truthful reporting and less than completely truthful reporting. It is shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) and mean square error (in terms of less than completely truthful reporting) than Kim and Elam's (2007) model. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM PUBLISHER]

In this article, a new randomized response model has been proposed. The proposed model is found to be more efficient than the randomized response models studied by Singh (2010). The relative efficiency of the proposed model has been studied with respect to the Singh (2010) model. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this article, a new “Partial” randomized response model has been proposed. Its properties are studied both theoretically and empirically. The proposed model is proved to be more efficient than the randomized response models studied by Eichhorn and Hayre (1983) and the “Partial” randomized response model. [ABSTRACT FROM AUTHOR]

This paper considers the use of stratified random sampling with proportional as well as Neyman allocations to unrelated question randomized response strategy. It has been shown that, for the prior information given, our new model is more efficient in terms of variance (in the case of completely truthful reporting) and mean square error (in case of less than completely truthful reporting). Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper proposes an alternative two-stage stratified randomized response model based on Tracy and Osahan (1999) model that has an optimal allocation and large gain in precision. It is also shown that the proposed model is more efficient than Kim and Warde (2004) and Kim and Elam (2005) stratified randomized response models under the conditions presented in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM PUBLISHER]

In this paper we have suggested a class of unbiased estimators of πS, the proportion of respondents possessing a sensitive attribute A using mixed randomized response model. The variance of the proposed class of estimators has been obtained. In addition to Kim and Warde's (2005) estimator, several other acceptable estimators of πShave been identified from the proposed class for suitable weights. It has been shown that the newly identified estimators are more efficient than the Kim and Warde's (2005) estimator. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper, an improved new additive model has been proposed. The proposed model is found to be more efficient than the randomized response models studied by Gjestvang and Singh (2009) and Singh (2010). The relative efficiency of the proposed model has been studied with respect to the Gjestvang and Singh (2009) and Singh (2010) model. It is found that the envisaged model is superior to those additive models earlier considered by Gjestvang and Singh (2009) and Singh (2010).Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this article, we have suggested a new modified mixed randomized response (RR) model and studied its properties. It is shown that the proposed mixed RR model is always more efficient than the Kim and Warde’s mixed RR model. The proposed mixed RR model has also been extended to stratified sampling. Numerical illustrations and graphical representations are also given in support of this study. [ABSTRACT FROM AUTHOR]

In this article, some new randomized response models have been proposed. Properties of the proposed randomized response models have been studied. The proposed models are found to be more efficient than the randomized response models studied by Himmelfarb and Edgell [1], Gjestvang and Singh [2] and Singh [3] under certain realistic conditions. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

The crux of this paper is to estimate the mean of the number of persons possessing a rare sensitive attribute based on the Singh et al. [1] randomization device by utilizing the Poisson distribution in stratified sampling. This study also deals with the extension of the estimation reported by Singh and Tarray [2] using a Poisson distribution and an unrelated question randomized response model reported in Singh et al. [1]. In stratified sampling, the estimators are proposed when the parameter of the rare unrelated attribute is known and also when it is unknown. It is shown that the proposed models are more efficient than the model given by Lee et al. [3] in both cases, that is, when the proportion of persons possessing a rare unrelated attribute is known and that when it is unknown. When the sizes of the stratified populations are not given, other estimators are suggested using stratified double sampling. Properties of the proposed randomized response model are studied and recommendations are made. [ABSTRACT FROM AUTHOR]

Taking the clue from the pioneer work of Hussain et al.'s (2014) we have suggested a new stratified randomized response model. The properties of the suggested stratified randomized response model have been studied under proportional and "Neyman" allocations. The study has been also carried out using modified Neyman allocation in the presence of crude prior estimates. Numerical illustrations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

In this paper, a simple and obvious procedure is presented that allows to estimate the population proportion π possessing sensitive attribute using simple random sampling with replacement (SRSWR). In addition to T, the probability that a respondent truthfully states that he or she bears a sensitive character when experienced in a direct response survey. An efficiency comparison is carried out to investigate in the performance of the proposed method. It is found that the proposed strategy is more efficient than Warner's (1965) as well as Huang's (2004) randomized response techniques under some realistic conditions. Numerical illustrations and graphical representations are also given in support of the present study. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of estimating the population proportion π_{S} possessing the sensitive attribute using stratified random sampling. Two alternatives to Kim and Warde [12] stratified randomized response model have been proposed. The proposed models are based on extending Singh [28] models to the case of stratified random sampling. It has been shown that the proposed models are more efficient than Kim and Warde [12] stratified randomized response technique under some realistic conditions in both the cases of completely truthful reporting and that of not completely truthful reporting by the respondents. A practical problem with the use of optimum (Neyman) allocation has been pointed out. Thus, in practice, the use of either proportional allocation or equal allocation has been suggested while estimating proportion of a sensitive attribute using stratified randomized response sampling. Numerical illustrations and graphs are also given in support of the present study. [ABSTRACT FROM AUTHOR]