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An Efficient Class of Calibration Ratio Estimators of Domain Mean in Survey Sampling

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摘要 This paper develops a new approach to domain estimation and proposes a new class of ratio estimators that is more efficient than the regression estimator and not depending on any optimality condition using the principle of calibration weightings.Some wellknown regression and ratio-type estimators are obtained and shown to be special members of the newclass of estimators.Results of analytical study showed that the new class of estimators is superior in both efficiency and biasedness to all related existing estimators under review.The relative performances of the new class of estimators with a corresponding global estimator were evaluated through a simulation study.Analysis and evaluation are presented.
出处 《Communications in Mathematics and Statistics》 SCIE 2020年第3期279-293,共15页 数学与统计通讯(英文)
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