Enhanced Mean Ratio Estimators of Auxiliary Variables Based on the Linear Mixture of Variances

Abstract

This paper incorporates the variance of auxiliary variables to propose three improved ratio estimators of population mean. To enhance the efficiency of the proposed ratio estimators, a linear combination of the population coefficient of variation, kurtosis, skewness and the population variance of the auxiliary variable is harnessed. The properties relating to the suggested estimators are assessed using constant, bias and mean square error. We also provided practical study for illustration and corroboration using a population data consisting of the fixed capital, which is the supporting variable and output of 80 factories which are the study variables. The suggested improved ratio estimators performed better than other ratio estimators in the literature when compared using bias and mean square error.