On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models
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Publication:722722
DOI10.1007/s00180-017-0773-8zbMath1417.65056OpenAlexW2769378428MaRDI QIDQ722722
Heidi Seibold, Riccardo De Bin, Anne-Laure Boulesteix, Christoph Bernau
Publication date: 27 July 2018
Published in: Computational Statistics (Search for Journal in Brave)
Full work available at URL: https://www.zora.uzh.ch/id/eprint/142477/1/180_2017_773_OnlinePDF.pdf
Computational methods for problems pertaining to statistics (62-08) Nonparametric regression and quantile regression (62G08) Linear regression; mixed models (62J05) Applications of statistics to biology and medical sciences; meta analysis (62P10) Censored data models (62N01) Generalized linear models (logistic models) (62J12)
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Uses Software
Cites Work
- Greedy function approximation: A gradient boosting machine.
- Model-based boosting in R: a hands-on tutorial using the R package mboost
- Boosting algorithms: regularization, prediction and model fitting
- Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages \textit{CoxBoost} and \textit{mboost}
- Least angle regression. (With discussion)
- Early stopping in \(L_{2}\)Boosting
- Boosting for high-dimensional linear models
- Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples
- Survival ensembles
- Boosting With theL2Loss
- Consistent Estimation of the Expected Brier Score in General Survival Models with Right‐Censored Event Times
- Generalized Additive Modeling with Implicit Variable Selection by Likelihood‐Based Boosting
- The elements of statistical learning. Data mining, inference, and prediction
