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This paper aims to discuss the problem of estimating the population mean Y ¯ of the study variable y using information on an auxiliary variable x in circular systematic sampling (CSS) scheme along with the non-response problem. We have suggested a generalized family of estimators for population mean Y ¯ of y based on auxiliary variable x. We have derived the expressions for bias and mean squared error of the proposed family of estimators upto first order of approximation. Optimum conditions are obtained in which the suggested family of estimators has the least mean squared error. It has been shown that the proposed family of estimators is better than usual unbiased estimator, ratio estimator and the regression estimator. An empirical study is provided to show that the suggested family of estimators is better than the ratio and regression estimators. [ABSTRACT FROM AUTHOR]

In this paper we have considered the problem of estimating the population mean of the sensitive variable using auxiliary information in sample surveys. We have suggested a class of ratio-cum-ratio-type exponential estimator for the population mean of the sensitive variable based on auxiliary variable. We have derived the bias and mean squared error of the proposed class of estimators up to first and second order of approximation. Conditions are obtained in which the suggested class of estimators is more efficient than the usual unbiased estimators, ratio estimator due to Sousa et al. (2010) and ratio-type-exponential estimator. Optimum estimator in the proposed class of estimators is also obtained. Numerical study is carried out in the support of the present study. [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]

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]