Journal
STATISTICAL MODELLING
Volume 18, Issue 3-4, Pages 365-384Publisher
SAGE PUBLICATIONS LTD
DOI: 10.1177/1471082X17748086
Keywords
variable selection; high-dimensional data; model choice; statistical learning
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Funding
- Deutsche Forschungsgemeinschaft (DFG) [SCHM 2966/1-2]
- Interdisciplinary Center for Clinical Research (IZKF) of the Friedrich-Alexander-University Erlangen-Nurnberg [J49]
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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.
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