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Composite kernel quantile regression
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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Bang, Sungwan | - |
| dc.contributor.author | Eo, Soo-Heang | - |
| dc.contributor.author | Jhun, Myoungshic | - |
| dc.contributor.author | Cho, Hyung Jun | - |
| dc.date.accessioned | 2021-09-03T14:54:50Z | - |
| dc.date.available | 2021-09-03T14:54:50Z | - |
| dc.date.created | 2021-06-16 | - |
| dc.date.issued | 2017 | - |
| dc.identifier.issn | 0361-0918 | - |
| dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/86282 | - |
| dc.description.abstract | The composite quantile regression (CQR) has been developed for the robust and efficient estimation of regression coefficients in a liner regression model. By employing the idea of the CQR, we propose a new regression method, called composite kernel quantile regression (CKQR), which uses the sum of multiple check functions as a loss in reproducing kernel Hilbert spaces for the robust estimation of a nonlinear regression function. The numerical results demonstrate the usefulness of the proposed CKQR in estimating both conditional nonlinear mean and quantile functions. | - |
| dc.language | English | - |
| dc.language.iso | en | - |
| dc.publisher | TAYLOR & FRANCIS INC | - |
| dc.subject | VARIABLE SELECTION | - |
| dc.subject | LINEAR-MODELS | - |
| dc.subject | EFFICIENT | - |
| dc.title | Composite kernel quantile regression | - |
| dc.type | Article | - |
| dc.contributor.affiliatedAuthor | Jhun, Myoungshic | - |
| dc.contributor.affiliatedAuthor | Cho, Hyung Jun | - |
| dc.identifier.doi | 10.1080/03610918.2015.1039133 | - |
| dc.identifier.scopusid | 2-s2.0-84996671410 | - |
| dc.identifier.wosid | 000398114600039 | - |
| dc.identifier.bibliographicCitation | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.46, no.3, pp.2228 - 2240 | - |
| dc.relation.isPartOf | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
| dc.citation.title | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION | - |
| dc.citation.volume | 46 | - |
| dc.citation.number | 3 | - |
| dc.citation.startPage | 2228 | - |
| dc.citation.endPage | 2240 | - |
| dc.type.rims | ART | - |
| dc.type.docType | Article | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Mathematics | - |
| dc.relation.journalWebOfScienceCategory | Statistics & Probability | - |
| dc.subject.keywordPlus | VARIABLE SELECTION | - |
| dc.subject.keywordPlus | LINEAR-MODELS | - |
| dc.subject.keywordPlus | EFFICIENT | - |
| dc.subject.keywordAuthor | Composite quantile regression | - |
| dc.subject.keywordAuthor | Kernel | - |
| dc.subject.keywordAuthor | Nonparametric estimation | - |
| dc.subject.keywordAuthor | Regularization | - |
| dc.subject.keywordAuthor | Ridge regression | - |
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Related Researcher
- CHO, HYUNG JUN
- College of Political Science & Economics (Department of Statistics)