Improved Estimation of Population Mean in Simple Random Sampling Using Attribute
Keywords:
Auxiliary attribute, bias, efficiency, mean square errorAbstract
This article discusses the problem of estimation of population mean using the information on an auxiliary attribute under simple random sampling. An improved class of estimator is suggested and the expression of the mean square error is determined up to the first order of approximation using Taylor series method. The usual mean estimator, classical ratio estimator envisaged by Naik and Gupta (1996) and Abd-Elfattah et al. (2010) estimators are identified as the members of the proposed class of estimator. The theoretical conditions are obtained by comparing the mean square error of the proposed estimators with the mean square error of the existing estimators under which the proposed class of estimator dominates the existing estimators suggested till date. A numerical study using real populations and a simulation study using artificially generated populations are conducted to enhance the theoretical findings.
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