Extending Statistical Boosting - An Overview of Recent Methodological Developments

Abstract

Boosting algorithms to simultaneously estimate and select predictor effects in statistical models have gained substantial interest during the last decade. This review article aims to highlight recent methodological developments regarding boosting algorithms for statistical modelling especially focusing on topics relevant for biomedical research. We suggest a unified framework for gradient boosting and likelihood-based boosting (statistical boosting) which have been addressed strictly separated in the literature up to now. Statistical boosting algorithms have been adapted to carry out unbiased variable selection and automated model choice during the fitting process and can nowadays be applied in almost any possible type of regression setting in combination with a large amount of different types of predictor effects. The methodological developments on statistical boosting during the last ten years can be grouped into three different lines of research: (i) efforts to ensure variable selection leading to sparser models, (ii) developments regarding different types of predictor effects and their selection (model choice), (iii) approaches to extend the statistical boosting framework to new regression settings.

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Cited by in corpus (6)

• The Evolution of Boosting Algorithms - From Machine Learning to Statistical Modelling
• A review of predictive uncertainty estimation with machine learning
• Boosting algorithms in energy research: A systematic review
• Stability selection for component-wise gradient boosting in multiple dimensions
• Predicting time to graduation at a large enrollment American university
• An update on statistical boosting in biomedicine