An update on statistical boosting in biomedicine. (English) Zbl 1397.92018
Summary: Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine learning approach with classical statistical modelling, offering various practical advantages like automated variable selection and implicit regularization of effect estimates. They are extremely flexible, as the underlying base-learners (regression functions defining the type of effect for the explanatory variables) can be combined with any kind of loss function (target function to be optimized, defining the type of regression setting). In this review article, we highlight the most recent methodological developments on statistical boosting regarding variable selection, functional regression, and advanced time-to-event modelling. Additionally, we provide a short overview on relevant applications of statistical boosting in biomedicine.
MSC:
| 92B15 | General biostatistics |
| 62P10 | Applications of statistics to biology and medical sciences; meta analysis |
| 62J05 | Linear regression; mixed models |
| 68W25 | Approximation algorithms |
Software:
R; pffr; CoxBoost; JMboost; datamicroarray; XGBoost; hdi; GBMCI; mboost; PBoostGA; JM; FDboost; AdaBoost.MH; ElemStatLearn; GAMBoost; GAMLSS; gamboostLSS; fda (R)References:
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