Abstract
In this study, we propose a new improved estimator of population mean for the sensitive variable in the presence of measurement error under simple and stratified random sampling. This estimator accounts the auxiliary information as well as the ranks of the auxiliary variable. From theoretical and numerical studies it is shown that a new improved estimator performs better than the existing estimators under study.
Highlights
In survey sampling, the assumption is made that all the observations are carefully considered on the characteristics under study so the information we obtained is error free
It is assumed that all observations based on the study variable and the auxiliary variable are observed without any error
We use the dummy variables and observations are recorded in terms of values of dummy variables. (ii) In application, some variables are clearly defined but it is hard to take the correct observations. (iii) It is no doubt that some variables are conceptually defined but is hard to take correct observation on it, instead the observations are taken on closely related variables
Summary
The assumption is made that all the observations are carefully considered on the characteristics under study so the information we obtained is error free. Not much literature has been found in estimating the population mean for the sensitive variable in the presence of measurement error based on dual use of the auxiliary information. The present paper is organized as: Section 2 gives existing estimators and an improved proposed estimator of population mean for sensitive variable in the presence of measurement error under simple random sampling.
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