Boosting for statistical modelling: A non-technical introduction

Mayr A, Hofner B (2018)

Publication Type: Journal article

Publication year: 2018

Journal

Statistical Modelling SAGE Publications (UK and US)

Book Volume: 18

Pages Range: 365-384

Journal Issue: 3-4

DOI: 10.1177/1471082x17748086

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.

Authors with CRIS profile

Andreas Mayr Lehrstuhl für Biometrie und Epidemiologie Benjamin Hofner

Involved external institutions

Paul Ehrlich Institute / Paul-Ehrlich-Institut – Bundesinstitut für Impfstoffe und biomedizinische Arzneimittel (PEI) DE Germany (DE)

How to cite

APA:

Mayr, A., & Hofner, B. (2018). Boosting for statistical modelling: A non-technical introduction. Statistical Modelling, 18(3-4), 365-384. https://doi.org/10.1177/1471082x17748086

MLA:

Mayr, Andreas, and Benjamin Hofner. "Boosting for statistical modelling: A non-technical introduction." Statistical Modelling 18.3-4 (2018): 365-384.

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