The benefit of data-based model complexity selection via prediction error curves in time-to-event data
No abstract is available for this item.
Volume (Year): 26 (2011)
Issue (Month): 2 (June)
|Contact details of provider:|| Web page: http://www.springerlink.com/link.asp?id=120306|
|Order Information:||Web: http://link.springer.de/orders.htm|
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Zhu, Mu, 2008. "Kernels and Ensembles: Perspectives on Statistical Learning," The American Statistician, American Statistical Association, vol. 62, pages 97-109, May.
- Ishwaran, Hemant & Kogalur, Udaya B. & Gorodeski, Eiran Z. & Minn, Andy J. & Lauer, Michael S., 2010. "High-Dimensional Variable Selection for Survival Data," Journal of the American Statistical Association, American Statistical Association, vol. 105(489), pages 205-217.
- Binder Harald & Schumacher Martin, 2008. "Adapting Prediction Error Estimates for Biased Complexity Selection in High-Dimensional Bootstrap Samples," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 7(1), pages 1-28, March.
When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:26:y:2011:i:2:p:293-302. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Guenther Eichhorn)or (Christopher F Baum)
If references are entirely missing, you can add them using this form.