Estimating Mean Using Auxiliary Information Under Measurement Errors
DOI:
https://doi.org/10.17762/msea.v71i4.793Abstract
The present paper concerns to the estimation of population mean using auxiliary information in presence of measurements errors. This is vital since it is common in everyday situations for data to be wrong and contain measurement errors for a variety of reasons. An estimator for approximating finite population mean is proposed in the presence of measurement errors. Up to first order of approximation, the proposed estimator's bias and mean squared error (MSE) expressions are obtained. It is concluded that the proposed estimator is more efficient than the existing estimator of population mean under measurement errors based on a theoretical comparison of their relative performance between the two types of estimators. To support the theoretical conclusions, an empirical study based on real data is conducted.