View : 247 Download: 88

Full metadata record

DC Field Value Language
dc.contributor.author유재근-
dc.date.accessioned2019-10-29T16:30:22Z-
dc.date.available2019-10-29T16:30:22Z-
dc.date.issued2019-
dc.identifier.issn2045-2322-
dc.identifier.otherOAK-25563-
dc.identifier.urihttp://dspace.ewha.ac.kr/handle/2015.oak/251633-
dc.description.abstractSufficient dimension reduction (SDR) for a regression pursue a replacement of the original p-dimensional predictors with its lower-dimensional linear projection. The so-called sliced inverse regression (SIR; [5]) arguably has the longest history in SDR methodologies, but it is still one of the most popular one. The SIR is known to be easily affected by the number of slices, which is one of its critical deficits. Recently, a fused approach for SIR is proposed to relieve this weakness, which fuses the kernel matrices computed by the SIR application from various numbers of slices. In the paper, the fused SIR is applied to a large-p-small n regression of a high-dimensional microarray right-censored data to show its practical advantage over usual SIR application. Through model validation, it is confirmed that the fused SIR outperforms the SIR with any number of slices under consideration. © 2019, The Author(s).-
dc.languageEnglish-
dc.publisherNature Publishing Group-
dc.titleAnalysis of microarray right-censored data through fused sliced inverse regression-
dc.typeArticle-
dc.relation.issue1-
dc.relation.volume9-
dc.relation.indexSCIE-
dc.relation.indexSCOPUS-
dc.relation.journaltitleScientific Reports-
dc.identifier.doi10.1038/s41598-019-51441-0-
dc.identifier.wosidWOS:000491306500003-
dc.identifier.scopusid2-s2.0-85073737571-
dc.author.googleYoo J.K.-
dc.contributor.scopusid유재근(23032759600)-
dc.date.modifydate20200120140526-


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE