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Logarithmic imputation techniques for temporal surveys: a memory-based approach explored through simulation and real-life applications

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  • Anoop Kumar

    (Central University of Haryana)

  • Shashi Bhushan

    (University of Lucknow)

Abstract

This research introduces memory-based logarithmic imputation techniques and the resulting estimators to address missing data within the temporal surveys. The mean square error of the resulting memory type estimators is reported to the first order approximation and the efficiency conditions are obtained by comparing the properties of the proposed and adapted imputation methods. The study contains a comprehensive simulation study to evaluate the performance of the resulting estimators under various conditions, providing insights into their applicability. Furthermore, the proposed methods are also illustrated through some real-life applications. The findings of simulation and real data application demonstrate the effectiveness of the memory type logarithmic imputation methods, providing insights into its application across different survey contexts and highlighting its potential to enhance data completeness and reliability in temporal survey analysis.

Suggested Citation

  • Anoop Kumar & Shashi Bhushan, 2025. "Logarithmic imputation techniques for temporal surveys: a memory-based approach explored through simulation and real-life applications," Quality & Quantity: International Journal of Methodology, Springer, vol. 59(3), pages 2707-2731, June.
    • Handle: RePEc:spr:qualqt:v:59:y:2025:i:3:d:10.1007_s11135-025-02096-9
    DOI: 10.1007/s11135-025-02096-9
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    References listed on IDEAS

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    1. Shashi Bhushan & Abhay Pratap Pandey, 2018. "Optimality of ratio type estimation methods for population mean in the presence of missing data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 47(11), pages 2576-2589, June.
    2. A. I. Al‐Omari & C. N. Bouza, 2015. "Ratio estimators of the population mean with missing values using ranked set sampling," Environmetrics, John Wiley & Sons, Ltd., vol. 26(2), pages 67-76, March.
    3. Awadhesh K. Pandey & G. N. Singh & D. Bhattacharyya & Pawan Kumar Singh, 2024. "Efficient and alternative approaches for imputing missing data to estimate population mean," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(6), pages 5883-5897, December.
    4. Irfan Aslam & Muhammad Noor-ul-Amin & Muhammad Hanif & Prayas Sharma, 2023. "Memory type ratio and product estimators under ranked-based sampling schemes," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 52(4), pages 1155-1177, February.
    5. 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.
    6. 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.
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