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Memory type estimators of population mean using exponentially weighted moving averages for time scaled surveys

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  • Muhammad Noor-ul-Amin

Abstract

Exponentially weighted moving average (EWMA) statistic is a memory type statistic that used present and past information to estimate the population parameter. This study utilizes EWMA statistic to propose a ratio and product estimator for the surveys based on time scale. The usual ratio and product estimators consist of only current sample information, whereas the proposed estimators consist of current as well as past sample information. The mean square error expressions of the proposed estimators are derived and mathematical conditions are established to prove the efficiency of proposed estimators. It is revealed from the results of simulation study that utilization of the past samples information excels the performance of estimator in terms of efficiency. Two real life examples are presented to demonstrate the use of proposed estimators.

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  • Muhammad Noor-ul-Amin, 2021. "Memory type estimators of population mean using exponentially weighted moving averages for time scaled surveys," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 50(12), pages 2747-2758, June.
    • Handle: RePEc:taf:lstaxx:v:50:y:2021:i:12:p:2747-2758
    DOI: 10.1080/03610926.2019.1670850
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    Cited by:

    1. Shashi Bhushan & Anoop Kumar & Amer Ibrahim Al-Omari & Ghadah A. Alomani, 2023. "Mean Estimation for Time-Based Surveys Using Memory-Type Logarithmic Estimators," Mathematics, MDPI, vol. 11(9), pages 1-14, April.

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