You can manage bookmarks using lists, please log in to your user account for this.
Media type:
E-Article
Title:
Boosting for statistical modelling-A non-technical introduction
Contributor:
Mayr, Andreas;
Hofner, Benjamin
Published:
SAGE Publications, 2018
Published in:
Statistical Modelling, 18 (2018) 3-4, Seite 365-384
Language:
English
DOI:
10.1177/1471082x17748086
ISSN:
1471-082X;
1477-0342
Origination:
Footnote:
Description:
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.