Search

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 suggested a class of estimators of population mean of sensitive variable under optional randomized response technique as reported in Gupta et al (2014). We have obtained the mean squared error (MSE) of the suggested class of estimators up to the first order of approximation. The optimum conditions are obtained at which the (MSE) of the proposed class of estimators is minimum. An empirical study is carried out to show the performance of the suggested class of estimators over existing estimators .It is found that the performance of proposed class of estimators is better than the existing estimators including Grover and Kaur (2019) [ABSTRACT FROM AUTHOR]

In this paper, the problem of estimating the population total of a sensitive quantitative variable has been considered. Odumade and Singh (2009) have given a RR model to estimate the population total of sensitive quantitative variable. We have given a modified RR model. It is noticed that the RR model envisaged by Odumade and Singh (2009) suffers with a difficulty that the optimum value of the constant involved in their RR model depends on the value of unknown parameter of the study variable, which lacks the utility of their RR model. To overcome such a difficulty Odumade and Singh (2009) have also suggested another estimator of the population total which depends on two RR models based on two independent random samples s1 and s2 from the population Ω using the sampling design p(s1) and p(s2) respectively; and also on a relation between the two constants involved in the two RR models. We should add here that drawing the two independent samples from a population constructing two RR models resultantly obtaining an estimator for a population total Y increase the cost of the survey. Keeping this in view we have suggested a RR model and developed an estimator for population total based on a single sample. The proposed RR model is free from the difficulty involved in Odumade and Singh (2009) model. We have shown theoretically that the proposed randomized response model is more efficient than Bar-Lev et al (2004) and Odumade and Singh (2009) RR models. This fact has also been supported through numerical illustration. [ABSTRACT FROM AUTHOR]

This paper proposes an efficient stratified randomized response model based on Chang et al.'s (2004) model. We have obtained the variance of the proposed estimator of πs, the proportion of the respondents in the population belonging to a sensitive group, under proportional and Neyman allocations. It is shown that the estimator based on the proposed model is more efficient than the Chang et al.'s (2004) estimator under both proportional as well as Neyman allocations, Hong et al.'s (1994) estimator and Kim and Warde's (2004) estimator. Numerical illustration and pictorial representation are given in support of the present study. [ABSTRACT FROM AUTHOR]

The present paper suggests an interesting and useful ramification of the unrelated randomized response model due to Pal and Singh (2012) [A new unrelated question randomized response model.Statistics46 (1), 99–109] that can be used for any sampling scheme. We have shown theoretically and numerically that the proposed model is more efficient than Pal and Singh (2012) model. [ABSTRACT FROM PUBLISHER]

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]

This paper addresses the problem of estimating the population mean of a sensitive variable in presence of scrambled response. We have suggested a randomized response model which uses known values of mean and variance of scrambling variable. We have shown that the proposed randomized response model is always better than that of Gjestvang and Singh's (2009) and Singh and Tarray's (2014) randomized response models. A numerical study for efficiency comparison is also presented. [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 considers the problem of estimating the population total of the sensitive variable under a unified approach. Odumade and Singh (2008) have suggested a forced quantitative randomized response model and then suggested an unbiased estimator of the population total Y of the sensitive variable y. It is observed that the optimum estimator of the population total Y due to Odumade and Singh (2008) depends on the unknown population parameter under investigation which lacks the practical utility. To overcome this drawback Odumade and Singh (2008) suggested another unbiased estimator of the population total of the sensitive variable based on two independent samples which causes the increase in the cost. Keeping this in view we define an alternative randomized response procedure and hence the estimator of the population total of the sensitive variable based on one sample only which is free from such limitation and more efficient than the Odumade and Singh (2008) estimator. The relative efficiency of the proposed estimator with respect to Bar-Lev et al. (2004) and Odumade and Singh (2008) estimators are examined through numerical illustration. [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]

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]

Randomized response models deal with stigmatizing variables appearing in health surveys. Additive and subtractive scrambling in split sample and double response yield unbiased mean and sensitivity estimators of high precision. The split sample method is protective of privacy. The double response method is as protective only conditionally. To achieve the maximum efficiency, the scrambling variables must be similar to each other and the probability of obtaining a true response must be as large as possible. The randomized response procedures yield more efficient estimates of the average total number of classes missed by university students. [ABSTRACT FROM AUTHOR]

This paper suggests an efficient randomized response model. We have studied the properties of the suggested randomized response model and compared with Gjestvang and Singh (2009) and Tarray and Singh (2015) model. We have shown theoretically and empirically that the proposed model is more efficient than the Gjestvang and Singh (2009) and Tarray and Singh (2015) randomized response models. [ABSTRACT FROM AUTHOR]

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]

This paper proposes classes of estimators for the mean of sensitive variable utilizing information on a non - sensitive auxiliary variable. Expressions for the biases and mean square errors of the suggested estimators correct up to first order of approximation are derived. It has been shown that the suggested new class of estimators based on the known population mean X and variance Sx 2 of the auxiliary variable X are better than conventional unbiased estimators which do not utilize the auxiliary information, Sousa et al.'s (2010) ratio estimator and Gupta et al.'s (2012) regression estimator under a very realistic condition. [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]