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Improvement in Regression Estimator of the Population Mean in Partial Quantitative Randomized Response Models

Nilgün Özgül, Hülya Çıngı

Abstract



Randomized Response Models (RRMs) are used to obtain reliable data with protecting the respondents confidentiality and avoid nonresponse rates when the sensitive questions are asked in surveys. Some studies have addressed situations in which the response to a sensitive quantitative variable. In this study, we have developed a new regression estimator for the population mean estimation of the sensitive study variable in Quantitative Randomized Response Models (QRRMs) using two non-sensitive auxiliary variables. The proposed estimator is compared with existing estimators in QRRMS with performing a simulation study and the proposed estimator is found be to more efficient than other existing estimators based on non-sensitive auxiliary variable.

Keywords


Quantitative sensitive variable, Randomized response models, Auxiliary variable, Regression estimator, Efficiency, Mean square error.

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