An update on statistical boosting in biomedicine
It addresses the need for updated insights on boosting techniques for researchers in biomedicine, but it is incremental as it reviews existing work rather than presenting new findings.
This review article summarizes recent methodological developments in statistical boosting algorithms, focusing on variable selection, functional regression, and time-to-event modeling, and provides an overview of their applications in biomedicine.
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.