An update on statistical boosting in biomedicine

TL;DR

This paper reviews recent methodological advances in statistical boosting algorithms, emphasizing their flexibility, variable selection, and applications in biomedicine, highlighting their growing importance in statistical modeling and machine learning.

Contribution

It provides an updated overview of developments in statistical boosting, focusing on variable selection, functional regression, and time-to-event modeling in biomedical research.

Findings

Advances in variable selection techniques for boosting.

Development of functional regression methods.

Application of boosting in biomedical time-to-event analysis.

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.