Enhancing Accuracy in Population Mean Estimation with Advanced Memory Type Exponential Estimators
DOI:
https://doi.org/10.13052/jrss0974-8024.1728Keywords:
Bias, Exponentially Weighted Moving Average (EWMA), Mean Square Error (MSE), Memory type estimator, Percent Relative Efficiency (PRE)Abstract
For a number of reasons, mean estimate is an essential sampling activity as it offers crucial information and forms the basis of statistical inference and judgement. In this study, we estimate the population mean using the Exponentially Weighted Moving Average (EWMA) statistic and provide generalized family of exponential estimators. The theoretical aspects of the suggested estimator are evaluated via rigorous mathematical derivations of the bias and mean square error (MSE), which are then compared to other exponential estimators that are already in use. Furthermore, a thorough simulation research is carried out to thoroughly assess the effectiveness and empirical performance of the suggested strategy. The results highlight how the estimator’s effectiveness is significantly increased when both recent and historical data are used in tandem.
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