A New Regression Type Estimator in the Presence of Auxiliary Information
Abstract
A new estimator has been proposed that combines the advantages of the regression estimator and the augmented mean estimator, improving the accuracy and efficiency of population parameter estimates in survey sampling and statistical inference. The proposed estimator uses a correction factor based on both the sample mean and the auxiliary variable and takes into account the correlation between the variables. This new estimator may outperform existing estimators in many situations and has been shown to reduce bias and increase the precision of estimation, leading to more accurate inferences and better decision-making. Simulated data comparing the new estimator to the sample mean and regression estimator shows that the new estimator is more efficient and has a lower mean squared error. These findings highlight the importance of considering auxiliary information and demonstrate the potential benefits of using the new estimator in practice.
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