title: Boosting - an unusual yet attractive optimiser.
abstract: This editorial is part of a For-Discussion-Section of Methods of Information in Medicine about the papers "The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling" and "Extending Statistical Boosting - An Overview of Recent Methodological Developments", written by Andreas Mayr and co-authors. It preludes two discussed reviews on developments and applications of boosting in biomedical research. The two review papers, written by Andreas Mayr, Harald Binder, Olaf Gefeller, and Matthias Schmid, give an overview on recently published methods that utilise gradient or likelihood-based boosting for fitting models in the life sciences. The reviews are followed by invited comments by experts in both boosting theory and applications.
authors.affiliation: Torsten Hothorn, Epidemiology, Biostatistics and Prevention Institute, University of Zurich, Hirschengraben 84, 8001 Zürich, Switzerland, E-Mail: Torsten.Hothorn@uzh.ch.
authors.forename: T
authors.initials: T
authors.lastname: Hothorn
mesh_terms: D000465:Algorithms; D035843:Biomedical Research / Q000706:statistics & numerical data*; D006801:Humans; D016013:Likelihood Functions; D000069550:Machine Learning; D015233:Models, Statistical