A fast algorithm for the accelerated failure time model with high-dimensional time-to-event data
DC Field | Value | Language |
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dc.contributor.author | Choi, Taehwa | - |
dc.contributor.author | Choi, Sangbum | - |
dc.date.accessioned | 2022-02-14T20:40:20Z | - |
dc.date.available | 2022-02-14T20:40:20Z | - |
dc.date.created | 2021-12-03 | - |
dc.date.issued | 2021-11-02 | - |
dc.identifier.issn | 0094-9655 | - |
dc.identifier.uri | https://scholar.korea.ac.kr/handle/2021.sw.korea/135780 | - |
dc.description.abstract | We propose the logistic-kernel smoothing procedure for the semiparametric accelerated failure time (AFT) model with high-dimensional right-censored data. The resulting estimating procedure permits fast and accurate computation of regression parameter estimates and standard errors while preserving the same asymptotic properties as those from the non-smoothed rank estimating function. In addition, we provide an efficient numerical algorithm for obtaining a complete regularization path to facilitate adaptive variable selection in the AFT model. This can be done by using a second-order approximation of the smoothed estimating function and coordinate decent algorithm. Through extensive simulation studies, we examine several well-known penalties and show that our method is robust and computationally efficient with minimal loss of precision. Application to primary biliary cirrhosis (PBC) data demonstrates the utility of the proposed method in routine survival data analysis. | - |
dc.language | English | - |
dc.language.iso | en | - |
dc.publisher | TAYLOR & FRANCIS LTD | - |
dc.subject | COORDINATE DESCENT ALGORITHMS | - |
dc.subject | LINEAR RANK-TESTS | - |
dc.subject | VARIABLE SELECTION | - |
dc.subject | ADAPTIVE LASSO | - |
dc.subject | REGULARIZED ESTIMATION | - |
dc.subject | SURVIVAL-DATA | - |
dc.subject | REGRESSION | - |
dc.title | A fast algorithm for the accelerated failure time model with high-dimensional time-to-event data | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Choi, Sangbum | - |
dc.identifier.doi | 10.1080/00949655.2021.1927034 | - |
dc.identifier.scopusid | 2-s2.0-85106480956 | - |
dc.identifier.wosid | 000654935900001 | - |
dc.identifier.bibliographicCitation | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION, v.91, no.16, pp.3385 - 3403 | - |
dc.relation.isPartOf | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.title | JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION | - |
dc.citation.volume | 91 | - |
dc.citation.number | 16 | - |
dc.citation.startPage | 3385 | - |
dc.citation.endPage | 3403 | - |
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 | ADAPTIVE LASSO | - |
dc.subject.keywordPlus | COORDINATE DESCENT ALGORITHMS | - |
dc.subject.keywordPlus | LINEAR RANK-TESTS | - |
dc.subject.keywordPlus | REGRESSION | - |
dc.subject.keywordPlus | REGULARIZED ESTIMATION | - |
dc.subject.keywordPlus | SURVIVAL-DATA | - |
dc.subject.keywordPlus | VARIABLE SELECTION | - |
dc.subject.keywordAuthor | Coordinate descent | - |
dc.subject.keywordAuthor | kernel smoothing | - |
dc.subject.keywordAuthor | linear model | - |
dc.subject.keywordAuthor | logistic loss | - |
dc.subject.keywordAuthor | regularization | - |
dc.subject.keywordAuthor | survival analysis | - |
dc.subject.keywordAuthor | variable selection | - |
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