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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| Format: | Article |
| Language: | English |
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Elsevier
2025-05-01
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| Series: | SoftwareX |
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| 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. |
| format | Article |
| id | doaj-art-aa339bbf3b614fa18b858d1f7f47b9f2 |
| institution | Kabale University |
| issn | 2352-7110 |
| language | English |
| publishDate | 2025-05-01 |
| publisher | Elsevier |
| record_format | Article |
| 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 |