The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
L1Penalized Estimation in the Cox Proportional Hazards Model
Abstract
This article presents a novel algorithm that efficiently computes L1 penalized (lasso) estimates of parameters in high‐dimensional models. The lasso has the property that it simultaneously performs variable selection and shrinkage, which makes it very useful for finding interpretable prediction rules in high‐dimensional data. The new algorithm is based on a combination of gradient ascent optimization with the Newton–Raphson algorithm. It is described for a general likelihood function and can be applied in generalized linear models and other models with an L1 penalty. The algorithm is demonstrated in the Cox proportional hazards model, predicting survival of breast cancer patients using gene expression data, and its performance is compared with competing approaches. An R package,penalized, that implements the method, is available on CRAN.
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Cites
Regression shrinkage and selection via the lasso
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Regularization and Variable Selection Via the Elastic Net
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Gene expression profiling predicts clinical outcome of breast cancer
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Model Selection and Estimation in Regression with Grouped Variables
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Gene-expression profiles to predict distant metastasis of lymph-node-negative primary breast cancer
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Gene-expression profiles predict survival of patients with lung adenocarcinoma
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The Group Lasso for Logistic Regression
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Landscape of genetic lesions in 944 patients with myelodysplastic syndromes
Haferlach, T., Nagata, Y., Grossmann, V, Okuno, Y., Bacher, U., Nagae, G., Schnittger, S., Sanada, M., Kon, A., Alpermann, T., et al. (2014) · Leukemia · doi:10.1038/leu.2013.336 · Cited by 1,532
Prospective Study on Circulating MicroRNAs and Risk of Myocardial Infarction
Zampetaki, A., Willeit, P., Tilling, L., Drozdov, I., Prokopi, M., Renard, J.-M., Mayr, A., Weger, S., Schett, G., Shah, A., et al. (2012) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2012.03.056 · Cited by 464
Lammers, W. J., Hirschfield, G. M., Corpechot, C., Nevens, F., Lindor, K. D., Janssen, H. L. A., Floreani, A., Ponsioen, C. Y., Mayo, M. J., Invernizzi, P., et al. (2015) · Gastroenterology · doi:10.1053/j.gastro.2015.07.061 · Cited by 441
Landscape Of Genetic Lesions In 944 Patients With Myelodysplastic Syndromes
Nagata, Y., Grossmann, V., Okuno, Y., Bacher, U., Nagae, G., Schnittger, S., Shiozawa, Y., Kon, A., Alpermann, T., Yoshida, K., et al. (2013) · Blood · doi:10.1182/blood.v122.21.521.521 · Cited by 431
Taskesen, E., Bullinger, L., Corbacioglu, A., Sanders, M. A., Erpelinck, C. A. J., Wouters, B. J., van der Poel-van de Luytgaarde, S. C., Damm, F., Krauter, J., Ganser, A., et al. (2011) · Blood · doi:10.1182/blood-2010-09-307280 · Cited by 384
Metadata
- Authors
- Goeman, J. J.
- Venue
- Biometrical Journal
- Publisher
- Wiley
- Published
- 2010-02
- Volume
- 52
- Issue
- 1
- Pages
- 70-84
- ISSN
- 0323-3847 (ISSN-L)
- 1521-4036
- OpenAlex
- W2021733365
- Cited by
- 816
- References
- 24
- FWCI
- 22.96
- Type
- journal article
- Language
- en
- Open access
- closed
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