Advanced Optional Randomized Response Models For Mean Estimation Under Non-Response And Measurement Error
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Abstract
A regression estimator for estimating the population mean of sensitive variable(s) in the presence of non-response and measurement error simultaneously using two scrambling variables are introduced. Comparisons are made with the mean squared error of the proposed estimator with some of the commonly used estimators i.e. Hansen and Hurwitz estimator, linear regression estimator and Diana et al. estimator. Under large sample approximation, their biases and mean square errors are estimated. In addition, an extensive simulation study with real and hypothetical population are also conducted to evaluate the performance of proposed estimator which show that these estimators perform better than the other considered estimators. A graphically representations are also used to represent the simulation results.
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