A numerical study on group quantile regression models
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kim, Doyoen | - |
dc.contributor.author | Jung, Yoonsuh | - |
dc.date.accessioned | 2021-09-01T13:29:18Z | - |
dc.date.available | 2021-09-01T13:29:18Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2019-07 | - |
dc.identifier.issn | 2287-7843 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/64655 | - |
dc.description.abstract | Grouping structures in covariates are often ignored in regression models. Recent statistical developments considering grouping structure shows clear advantages; however, reflecting the grouping structure on the quantile regression model has been relatively rare in the literature. Treating the grouping structure is usually conducted by employing a group penalty. In this work, we explore the idea of group penalty to the quantile regression models. The grouping structure is assumed to be known, which is commonly true for some cases. For example, group of dummy variables transformed from one categorical variable can be regarded as one group of covariates. We examine the group quantile regression models via two real data analyses and simulation studies that reveal the beneficial performance of group quantile regression models to the non-group version methods if there exists grouping structures among variables. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | KOREAN STATISTICAL SOC | - |
dc.subject | VARIABLE SELECTION | - |
dc.subject | LASSO | - |
dc.title | A numerical study on group quantile regression models | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jung, Yoonsuh | - |
dc.identifier.doi | 10.29220/CSAM.2019.26.4.359 | - |
dc.identifier.scopusid | 2-s2.0-85071765251 | - |
dc.identifier.wosid | 000483361400003 | - |
dc.identifier.bibliographicCitation | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS, v.26, no.4, pp.359 - 370 | - |
dc.relation.isPartOf | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS | - |
dc.citation.title | COMMUNICATIONS FOR STATISTICAL APPLICATIONS AND METHODS | - |
dc.citation.volume | 26 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 359 | - |
dc.citation.endPage | 370 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.identifier.kciid | ART002491406 | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scopus | - |
dc.description.journalRegisteredClass | kci | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordPlus | LASSO | - |
dc.subject.keywordAuthor | group penalty | - |
dc.subject.keywordAuthor | penalized quantile regression | - |
dc.subject.keywordAuthor | variable selection | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
145 Anam-ro, Seongbuk-gu, Seoul, 02841, Korea+82-2-3290-2963
COPYRIGHT © 2021 Korea University. All Rights Reserved.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.