@article{Kumar2025, 
author = {Anoop Kumar and Renu Kumari and Abdullah Mohammed Alomair},
title = {Optimal classes of memory-type estimators of population mean for temporal surveys},
year = {2025},
journal = {AIMS Mathematics},
volume = {10},
number = {1},
pages = {1008-1025},
keywords = {efficiency, simple random sampling, exponentially weighted moving average, mean square error, mean estimation},
url = {https://www.sciopen.com/article/10.3934/math.2025048},
doi = {10.3934/math.2025048},
abstract = {In this article, we explore how to efficiently estimate the population mean utilizing past and current sample information through exponentially weighted moving average (EWMA) statistics in temporal surveys. We propose some optimal classes of memory-type estimators of population mean for temporal surveys within the framework of simple random sampling (SRS). We derive the expressions for the bias and mean square error (MSE) of the suggested estimators up to first-order approximation. We compare the traditional and newly introduced memory-type estimators and establish the efficiency conditions. Moreover, we conduct a thorough simulation study using real and artificial populations to refine our theoretical outcomes. The simulation results show that studying past and current sample data increase the efficiency of the proposed estimators.}
}