dc.contributor.author | Mai, The Tien | |
dc.date.accessioned | 2024-06-05T12:29:19Z | |
dc.date.available | 2024-06-05T12:29:19Z | |
dc.date.created | 2024-05-24T09:54:36Z | |
dc.date.issued | 2024 | |
dc.identifier.citation | Statistica neerlandica 2024 | en_US |
dc.identifier.issn | 0039-0402 | |
dc.identifier.uri | https://hdl.handle.net/11250/3132713 | |
dc.description.abstract | In this study, we address the problem of high-dimensional binary classification. Our proposed solution involves employing an aggregation technique founded on exponential weights and empirical hinge loss. Through the employment of a suitable sparsity-inducing prior distribution, we demonstrate that our method yields favorable theoretical results on prediction error. The efficiency of our procedure is achieved through the utilization of Langevin Monte Carlo, a gradient-based sampling approach. To illustrate the effectiveness of our approach, we conduct comparisons with the logistic Lasso on simulated data and a real dataset. Our method frequently demonstrates superior performance compared to the logistic Lasso. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Wiley | en_US |
dc.relation.uri | https://doi.org/10.1111/stan.12342 | |
dc.rights | Navngivelse-Ikkekommersiell 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by-nc/4.0/deed.no | * |
dc.title | High-dimensional sparse classification using exponential weighting with empirical hinge loss | en_US |
dc.title.alternative | High-dimensional sparse classification using exponential weighting with empirical hinge loss | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.journal | Statistica neerlandica | en_US |
dc.identifier.doi | 10.1111/stan.12342 | |
dc.identifier.cristin | 2270621 | |
dc.relation.project | Norges forskningsråd: 309960 | en_US |
cristin.ispublished | false | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |