Please use this identifier to cite or link to this item:
http://hdl.handle.net/10397/93849
DC Field | Value | Language |
---|---|---|
dc.contributor | Department of Applied Mathematics | en_US |
dc.creator | Liu, L | en_US |
dc.creator | Liu, Y | en_US |
dc.creator | Su, F | en_US |
dc.creator | Zhao, X | en_US |
dc.date.accessioned | 2022-08-03T01:23:55Z | - |
dc.date.available | 2022-08-03T01:23:55Z | - |
dc.identifier.issn | 0319-5724 | en_US |
dc.identifier.uri | http://hdl.handle.net/10397/93849 | - |
dc.language.iso | en | en_US |
dc.publisher | John Wiley & Sons | en_US |
dc.rights | © 2020 Statistical Society of Canada | en_US |
dc.rights | This is the peer reviewed version of the following article: Liu, L., Liu, Y., Su, F., & Zhao, X. (2021). Variable selection and structure estimation for ultrahigh‐dimensional additive hazards models. Canadian Journal of Statistics, 49(3), 826-852, which has been published in final form at https://doi.org/10.1002/cjs.11593. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions. This article may not be enhanced, enriched or otherwise transformed into a derivative work, without express permission from Wiley or by statutory rights under applicable legislation. Copyright notices must not be removed, obscured or modified. The article must be linked to Wiley’s version of record on Wiley Online Library and any embedding, framing or otherwise making available the article or pages thereof by third parties from platforms, services and websites other than Wiley Online Library must be prohibited. | en_US |
dc.subject | Additive hazards regression | en_US |
dc.subject | Model pursuit | en_US |
dc.subject | Penalized sieve least squares | en_US |
dc.subject | Ultrahigh-dimensional censored data | en_US |
dc.subject | Variable selection | en_US |
dc.title | Variable selection and structure estimation for ultrahigh-dimensional additive hazards models | en_US |
dc.type | Journal/Magazine Article | en_US |
dc.identifier.spage | 826 | en_US |
dc.identifier.epage | 852 | en_US |
dc.identifier.volume | 49 | en_US |
dc.identifier.issue | 3 | en_US |
dc.identifier.doi | 10.1002/cjs.11593 | en_US |
dcterms.abstract | We develop a class of regularization methods based on the penalized sieve least squares for simultaneous model pursuit, variable selection, and estimation in high-dimensional additive hazards regression models. In the framework of sparse ultrahigh-dimensional models, the asymptotic properties of the proposed estimators include structure identification consistency and oracle variable selection. The computational process can be efficiently implemented by applying the blockwise majorization descent algorithm. Simulation studies demonstrate the performance of the proposed methodology, and the primary biliary cirrhosis data analysis is provided for illustration. | en_US |
dcterms.accessRights | open access | en_US |
dcterms.bibliographicCitation | Canadian journal of statistics, Sept. 2021, v. 49, no. 3, p. 826-852 | en_US |
dcterms.isPartOf | Canadian journal of statistics | en_US |
dcterms.issued | 2021-09 | - |
dc.identifier.scopus | 2-s2.0-85100141017 | - |
dc.identifier.eissn | 1708-945X | en_US |
dc.description.validate | 202208 bcfc | en_US |
dc.description.oa | Accepted Manuscript | en_US |
dc.identifier.FolderNumber | AMA-0014, a2342b | - |
dc.identifier.SubFormID | 47547 | - |
dc.description.fundingSource | RGC | en_US |
dc.description.pubStatus | Published | en_US |
dc.identifier.OPUS | 54170329 | - |
Appears in Collections: | Journal/Magazine Article |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
Zhao_Variable_Selection_Structure.pdf | Pre-Published version | 571.44 kB | Adobe PDF | View/Open |
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