This approach to fit a generalized linear model to high-dimensional data, with implicit variable selection, is computationally attractive. Fitting the model as ...
Nov 15, 2006 · Summary: The R add-on package mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions ...
We prove that boosting with the squared error loss, L2Boosting, is consistent for very high-dimensional linear models, where the num- ber of predictor ...
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The R add-on package mboost implements functional gradient descent algorithms (boosting) for optimizing general loss functions utilizing componentwise least ...
Publication status. published ; Journal / series. Bioinformatics ; Volume. 22 (22) ; Pages / Article No. 2828 - 2829 ; Publisher. Oxford University Press.
Model-based boosting in high dimensions. Authors. Hothorn, Torsten; Bühlmann, Peter. Abstract. The R add-on package mboost implements functional gradient ...
We describe version 2.0 of the R add-on package mboost. The package implements boosting for optimizing general risk functions using component-wise (penalized) ...
Sep 16, 2021 · Statistical boosting is a computational approach to select and estimate interpretable prediction models for high-dimensional biomedical data.
mboost implements boosting algorithms for fitting generalized linear, additive and interaction models to potentially high-dimensional data.
The package implements boosting for optimizing general risk functions utilizing component-wise (penalized) least squares estimates or regression trees as base- ...