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Computational and Mathematical Methods in Medicine
Volume 2017, Article ID 6083072, 12 pages
Review Article

An Update on Statistical Boosting in Biomedicine

1Institut für Medizininformatik, Biometrie und Epidemiologie, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
2Institut für Statistik, Ludwig-Maximilians-Universität München, Munich, Germany
3Paul-Ehrlich-Institut, Langen, Germany

Correspondence should be addressed to Andreas Mayr; ed.uaf@ryam.saerdna

Received 24 February 2017; Accepted 8 June 2017; Published 2 August 2017

Academic Editor: Andrzej Kloczkowski

Copyright © 2017 Andreas Mayr et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.