Novel Estimators for Enhanced Finite Population Variance Estimation with Auxiliary Data
Keywords:
Population variance, mean squared error, bias, auxiliary variable, study variableAbstract
This paper proposes two novel classes of efficient estimators for estimating the finite population variance under the Simple Random Sampling Without Replacement (SRSWOR) scheme by utilizing auxiliary information in sample surveys. The bias and mean squared error (MSE) expressions of the proposed classes of estimators are derived up to the first order of approximation. The optimum conditions under which the proposed estimators achieve minimum MSE are also obtained. Furthermore, regions of preference are established to determine the conditions under which the proposed estimators perform better than the existing estimators available in the literature. An empirical study based on three real data sets is carried out to evaluate the efficiency and practical applicability of the proposed estimators.
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