Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost-Reference-Cited by-同舟云学术

Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost

Published:2016-01-13 Issue:2 Volume:31 Page:513-531
ISSN:0943-4062
Container-title:Computational Statistics
language:en
Short-container-title:Comput Stat

Author:

De Bin Riccardo

Funder

Deutsche Forschungsgemeinschaft

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability

Link

http://link.springer.com/content/pdf/10.1007/s00180-015-0642-2.pdf

Reference36 articles.

1. Binder H (2013a) CoxBoost: Cox models by likelihood based boosting for a single survival endpoint or competing risks. R package version 1.4. http://CRAN.R-project.org/package=CoxBoost

2. Binder H (2013b) GAMBoost: generalized linear and additive models by likelihood based boosting. R package version 1.2-3. http://CRAN.R-project.org/package=GAMBoost

3. Binder H, Schumacher M (2008) Allowing for mandatory covariates in boosting estimation of sparse high-dimensional survival models. BMC Bioinform 9:14

4. Boulesteix AL, Hothorn T (2010) Testing the additional predictive value of high-dimensional molecular data. BMC Bioinform 11:78

5. Boulesteix AL, Sauerbrei W (2011) Added predictive value of high-throughput molecular data to clinical data and its validation. Brief Bioinform 12:215–229

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