Bergherr, Elisabeth

[["dc.bibliographiccitation.firstpage","422"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.lastpage","430"],["dc.bibliographiccitation.volume","55"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2021-09-17T09:11:09Z"],["dc.date.available","2021-09-17T09:11:09Z"],["dc.date.issued","2016-10-17"],["dc.description.abstract","Penalization and regularization techniques for statistical modeling have attracted increasing attention in biomedical research due to their advantages in the presence of high-dimensional data. A special focus lies on algorithms that incorporate automatic variable selection like the least absolute shrinkage operator (lasso) or statistical boosting techniques."],["dc.identifier.doi","10.3414/ME16-01-0033"],["dc.identifier.pmid","27626931"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89629"],["dc.language.iso","en"],["dc.relation.eissn","2511-705X"],["dc.relation.issn","0026-1270"],["dc.title","Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","6083072"],["dc.bibliographiccitation.journal","Computational and Mathematical Methods in Medicine"],["dc.contributor.author","Mayr, Andreas"],["dc.contributor.author","Hofner, Benjamin"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Meyer, Sebastian"],["dc.contributor.author","Gefeller, Olaf"],["dc.date.accessioned","2021-09-17T09:10:53Z"],["dc.date.available","2021-09-17T09:10:53Z"],["dc.date.issued","2017"],["dc.description.abstract","Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine."],["dc.identifier.doi","10.1155/2017/6083072"],["dc.identifier.pmid","28831290"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89626"],["dc.language.iso","en"],["dc.relation.eissn","1748-6718"],["dc.relation.issn","1748-670X"],["dc.title","An Update on Statistical Boosting in Biomedicine"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Computational Statistics"],["dc.contributor.author","Zhang, Boyao"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Greven, Sonja"],["dc.contributor.author","Bergherr, Elisabeth"],["dc.date.accessioned","2022-04-27T09:39:55Z"],["dc.date.available","2022-04-27T09:39:55Z"],["dc.date.issued","2022"],["dc.identifier.doi","10.1007/s00180-022-01199-3"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/106902"],["dc.language.iso","en"],["dc.relation.issn","0943-4062"],["dc.relation.issn","1613-9658"],["dc.relation.orgunit","Professur für Raumbezogene Datenanalyse und Statistische Lernverfahren"],["dc.title","Adaptive step-length selection in gradient boosting for Gaussian location and scale models"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","60"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Methods of Information in Medicine"],["dc.bibliographiccitation.volume","58"],["dc.contributor.author","Hepp, Tobias"],["dc.contributor.author","Schmid, Matthias"],["dc.contributor.author","Gefeller, Olaf"],["dc.contributor.author","Waldmann, Elisabeth"],["dc.contributor.author","Mayr, Andreas"],["dc.date.accessioned","2021-09-17T09:10:17Z"],["dc.date.available","2021-09-17T09:10:17Z"],["dc.date.issued","2019"],["dc.identifier.doi","10.1055/s-0038-1669389"],["dc.identifier.pmid","30634196"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/89619"],["dc.language.iso","en"],["dc.relation.eissn","2511-705X"],["dc.relation.issn","0026-1270"],["dc.title","Addendum to: Approaches to Regularized Regression - A Comparison between Gradient Boosting and the Lasso"],["dc.type","journal_article"],["dc.type.internalPublication","unknown"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]