Hierarchically penalized quantile regression
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Kang, Jongkyeong | - |
dc.contributor.author | Bang, Sungwan | - |
dc.contributor.author | Jhun, Myoungshic | - |
dc.date.accessioned | 2021-09-04T03:42:53Z | - |
dc.date.available | 2021-09-04T03:42:53Z | - |
dc.date.created | 2021-06-18 | - |
dc.date.issued | 2016-01-22 | - |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/89768 | - |
dc.description.abstract | In many regression problems, predictors are naturally grouped. For example, when a set of dummy variables is used to represent categorical variables, or a set of basis functions of continuous variables is included in the predictor set, it is important to carry out a feature selection both at the group level and at individual variable levels within the group simultaneously. To incorporate the group and variables within-group information into a regularized model fitting, several regularization methods have been developed, including the Cox regression and the conditional mean regression. Complementary to earlier works, the simultaneous group and within-group variables selection method is examined in quantile regression. We propose a hierarchically penalized quantile regression, and show that the hierarchical penalty possesses the oracle property in quantile regression, as well as in the Cox regression. The proposed method is evaluated through simulation studies and a real data application. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.subject | VARIABLE SELECTION | - |
dc.title | Hierarchically penalized quantile regression | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Jhun, Myoungshic | - |
dc.identifier.doi | 10.1080/00949655.2015.1014038 | - |
dc.identifier.scopusid | 2-s2.0-84943815668 | - |
dc.identifier.wosid | 000362557800009 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.86, no.2, pp.340 - 356 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.title | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.volume | 86 | - |
dc.citation.number | 2 | - |
dc.citation.startPage | 340 | - |
dc.citation.endPage | 356 | - |
dc.type.rims | ART | - |
dc.type.docType | Article | - |
dc.description.journalClass | 1 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science | - |
dc.relation.journalResearchArea | Mathematics | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Interdisciplinary Applications | - |
dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordAuthor | group variable selection | - |
dc.subject.keywordAuthor | hierarchical regularization | - |
dc.subject.keywordAuthor | linear programming | - |
dc.subject.keywordAuthor | quantile regression | - |
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.