Improved estimation of population mean based on hybrid exponentially weighted moving average
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Abstract
In sampling theory, the researchers are often dependent on estimators that use only current sample data to estimate population parameters. However, the hybrid exponentially weighted moving average (HEWMA) approach incorporates both current and past sample information and helps increasing the efficiency of the estimators. This enables us to develop an improved estimation procedure for temporal surveys based on HEWMA. We develop memory-type log estimator of population mean based on HEWMA under simple random sampling (SRS). We derive the bias and mean square error (MSE) of the developed estimator to the first-order approximation. The efficiency conditions are established by comparing the MSE of the proposed estimator with the MSE of the available traditional and memory-type estimators. To validate our theoretical findings, we conduct a simulation study utilizing hypothetically drawn population. A real data illustration of the developed methods is also presented. The findings demonstrate that our approach integrates past and present sample information and enhances the estimators’ efficacy.
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