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

Anoop Kumar () and Shashi Bhushan ()
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Anoop Kumar: Central University of Haryana
Shashi Bhushan: University of Lucknow

Quality & Quantity: International Journal of Methodology, 2025, vol. 59, issue 3, No 32, 2707-2731

Abstract: 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.

Keywords: Exponentially weighted moving average; Memory type ratio and product imputation methods; Mean square error; Relative efficiency; 62D10; 62D05 (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s11135-025-02096-9

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