TLIC: An R package for the LIC for T distribution regression analysis

This paper introduces the TLIC R package, a novel framework that integrates the T-distribution with the Length and Information Criterion (LIC) to address optimal subset selection in regression models with T-distributed errors. Traditional subset selection methods, such as beta_AD, beta_cor, and LICn...

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Main Authors: Guofu Jing, Guangbao Guo
Format: Article
Language:English
Published: Elsevier 2025-05-01
Series:SoftwareX
Subjects:
Online Access:http://www.sciencedirect.com/science/article/pii/S2352711025000998
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author Guofu Jing
Guangbao Guo
author_facet Guofu Jing
Guangbao Guo
author_sort Guofu Jing
collection DOAJ
description This paper introduces the TLIC R package, a novel framework that integrates the T-distribution with the Length and Information Criterion (LIC) to address optimal subset selection in regression models with T-distributed errors. Traditional subset selection methods, such as beta_AD, beta_cor, and LICnew, assume normality of errors, which may lead to biased results when dealing with heavy-tailed or skewed distributions. Through extensive simulation experiments, we demonstrate that TLIC outperforms these methods in terms of stability and sensitivity, especially under non-normal error distributions. An R package implementing the TLIC method is also developed, providing a practical tool for researchers to conduct subset selection with T-distributed errors. Our findings highlight TLIC's potential to improve subset selection accuracy in real-world applications where error distributions deviate from normality.
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institution Kabale University
issn 2352-7110
language English
publishDate 2025-05-01
publisher Elsevier
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series SoftwareX
spelling doaj-art-aa339bbf3b614fa18b858d1f7f47b9f22025-08-20T03:48:14ZengElsevierSoftwareX2352-71102025-05-013010213210.1016/j.softx.2025.102132TLIC: An R package for the LIC for T distribution regression analysisGuofu Jing0Guangbao Guo1School of Mathematics and Statistics, Shandong University of Technology, Zibo, ChinaCorresponding author.; School of Mathematics and Statistics, Shandong University of Technology, Zibo, ChinaThis paper introduces the TLIC R package, a novel framework that integrates the T-distribution with the Length and Information Criterion (LIC) to address optimal subset selection in regression models with T-distributed errors. Traditional subset selection methods, such as beta_AD, beta_cor, and LICnew, assume normality of errors, which may lead to biased results when dealing with heavy-tailed or skewed distributions. Through extensive simulation experiments, we demonstrate that TLIC outperforms these methods in terms of stability and sensitivity, especially under non-normal error distributions. An R package implementing the TLIC method is also developed, providing a practical tool for researchers to conduct subset selection with T-distributed errors. Our findings highlight TLIC's potential to improve subset selection accuracy in real-world applications where error distributions deviate from normality.http://www.sciencedirect.com/science/article/pii/S2352711025000998R packageT-distributionOptimal subset selection
spellingShingle Guofu Jing
Guangbao Guo
TLIC: An R package for the LIC for T distribution regression analysis
SoftwareX
R package
T-distribution
Optimal subset selection
title TLIC: An R package for the LIC for T distribution regression analysis
title_full TLIC: An R package for the LIC for T distribution regression analysis
title_fullStr TLIC: An R package for the LIC for T distribution regression analysis
title_full_unstemmed TLIC: An R package for the LIC for T distribution regression analysis
title_short TLIC: An R package for the LIC for T distribution regression analysis
title_sort tlic an r package for the lic for t distribution regression analysis
topic R package
T-distribution
Optimal subset selection
url http://www.sciencedirect.com/science/article/pii/S2352711025000998
work_keys_str_mv AT guofujing tlicanrpackageforthelicfortdistributionregressionanalysis
AT guangbaoguo tlicanrpackageforthelicfortdistributionregressionanalysis