Log-transformed approaches to variance estimation using auxiliary data

Singh, P; Sharma, P; Singh, A; Zaman, T; Emam, W

Log-transformed approaches to variance estimation using auxiliary data

Singh, P Sharma, P Singh, A Zaman, T Emam, W

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Publisher:: Elsevier
Publication Type:: Journal Article
Citation:: Journal of Radiation Research and Applied Sciences, 2025, 18, (4), pp. 101913
Issue Date:: 2025-12

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Field	Value	Language
dc.contributor.author	Singh, P
dc.contributor.author	Sharma, P
dc.contributor.author	Singh, A
dc.contributor.author	Zaman, T
dc.contributor.author	Emam, W
dc.date.accessioned	2026-07-13T01:59:27Z
dc.date.available	2026-07-13T01:59:27Z
dc.date.issued	2025-12
dc.identifier.citation	Journal of Radiation Research and Applied Sciences, 2025, 18, (4), pp. 101913
dc.identifier.issn	1687-8507
dc.identifier.uri	http://hdl.handle.net/10453/195625
dc.description.abstract	Reliable statistical inference requires accurate population variance estimate, especially in domains where measurement limitations and intrinsic variability impact data. Applying traditional estimators to skewed or heavy-tailed populations frequently results in inefficient results. We provide a novel class of generalized logarithmic variance estimators that use logarithmic transformations and auxiliary data to stabilize variance and improve estimator performance in order to overcome this constraint. We calculate the suggested estimators’ bias and Mean Squared Error (MSE) expressions and assess their effectiveness using comprehensive Monte Carlo simulations with different sample sizes. The suggested estimator, T r 1 − , produces a significant reduction in MSE up to 57% increase in efficiency (PRE) at n=600 when compared to the usual estimator. The suggested methodologies resilience and suitability for high-variability situations are further confirmed using real-world datasets. In every context, the findings show that the suggested estimators perform better than the current ones in terms of lower MSE and greater PRE. This research demonstrates how logarithmic transformations may be used to create variance estimators that are more accurate and effective, especially when auxiliary variables are provided.
dc.language	en
dc.publisher	Elsevier
dc.relation.ispartof	Journal of Radiation Research and Applied Sciences
dc.relation.isbasedon	10.1016/j.jrras.2025.101913
dc.rights	info:eu-repo/semantics/openAccess
dc.title	Log-transformed approaches to variance estimation using auxiliary data
dc.type	Journal Article
utslib.citation.volume	18
pubs.organisational-group	University of Technology Sydney
pubs.organisational-group	University of Technology Sydney/Faculty of Science
utslib.copyright.status	open_access	*
dc.rights.license	This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). To view a copy of this license, visit https://creativecommons.org/licenses/by/4.0/
dc.date.updated	2026-07-13T01:59:25Z
pubs.issue	4
pubs.publication-status	Published
pubs.volume	18
utslib.citation.issue	4

Abstract:

Reliable statistical inference requires accurate population variance estimate, especially in domains where measurement limitations and intrinsic variability impact data. Applying traditional estimators to skewed or heavy-tailed populations frequently results in inefficient results. We provide a novel class of generalized logarithmic variance estimators that use logarithmic transformations and auxiliary data to stabilize variance and improve estimator performance in order to overcome this constraint. We calculate the suggested estimators’ bias and Mean Squared Error (MSE) expressions and assess their effectiveness using comprehensive Monte Carlo simulations with different sample sizes. The suggested estimator, T r 1 − , produces a significant reduction in MSE up to 57% increase in efficiency (PRE) at n=600 when compared to the usual estimator. The suggested methodologies resilience and suitability for high-variability situations are further confirmed using real-world datasets. In every context, the findings show that the suggested estimators perform better than the current ones in terms of lower MSE and greater PRE. This research demonstrates how logarithmic transformations may be used to create variance estimators that are more accurate and effective, especially when auxiliary variables are provided.

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http://hdl.handle.net/10453/195625