Boosting for statistical modelling: A non-technical introduction

Mayr, Andreas und Hofner, Benjamin (2018): Boosting for statistical modelling: A non-technical introduction. In: Statistical Modelling, Bd. 18, Nr. 3-4: S. 365-384

Abstract

Boosting algorithms were originally developed for machine learning but were later adapted to estimate statistical models-offering various practical advantages such as automated variable selection and implicit regularization of effect estimates. The interpretation of the resulting models, however, remains the same as if they had been fitted by classical methods. Boosting, hence, allows to use an advanced machine learning scheme to estimate various types of statistical models. This tutorial aims to highlight how boosting can be used for semi-parametric modelling, what practical implications follow from the design of the algorithm and what kind of drawbacks data analysts have to expect. We illustrate the application of boosting in the analysis of a stunting score from children in India and a high-dimensional dataset of tumour DNA to develop a biomarker for the occurrence of metastases in breast cancer patients.

• Dokumententyp: Zeitschriftenartikel
• Fakultät: Mathematik, Informatik und Statistik > Mathematik
• Themengebiete: 500 Naturwissenschaften und Mathematik > 510 Mathematik
• ISSN: 1471-082X
• Allianz-/Nationallizenz: Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG-geförderten) Allianz- bzw. Nationallizenz frei zugänglich.
• Sprache: Englisch