The benefit of data-based model complexity selection via prediction error curves in time-to-event data
AbstractNo abstract is available for this item.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoArticle provided by Springer in its journal Computational Statistics.
Volume (Year): 26 (2011)
Issue (Month): 2 (June)
Contact details of provider:
Web page: http://www.springerlink.com/link.asp?id=120306
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.
- 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.
- 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.
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.