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A class of ratio type estimators in double sampling using an auxiliary variable with some known population parameters


Toluwalase J. Akingbade and Fabian C. Okafor

YEAR: 2018


Many authors, to improve on the ratio (regression) estimator of the population mean of the study variable, have made use of some known population parameters of the auxiliary variable including the population mean of the auxiliary variable. In their work these authors have used the ratio (regression) – type estimator. In practical situations, the population mean of the auxiliary variable may not be known. In this paper therefore, we have suggested a class of regression - type ratio estimator when only the population mean of the auxiliary variable is unknown using double sampling procedure. The expression for the bias and the mean square error of the proposed estimators were derived and sub-members of the class of estimators were also identified. The conditions for which the proposed estimators perform better than the sample mean, classical ratio and existing ratio- type estimator in double sampling are derived. From the analysis, it was observed that the proposed estimators perform better than the existing estimators considered in this study.


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