Logarithmic imputation techniques for temporal surveys: a memory‑based approach explored through simulation and real‑life applications
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Date
2025
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Abstract
This research introduces memory-based logarithmic imputation techniques and the result
ing 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.