IDEAS home Printed from
   My bibliography  Save this article

Univariate Shrinkage in the Cox Model for High Dimensional Data


  • Tibshirani Robert J.

    (Stanford University)


We propose a method for prediction in Cox's proportional model, when the number of features (regressors), p, exceeds the number of observations, n. The method assumes that the features are independent in each risk set, so that the partial likelihood factors into a product. As such, it is analogous to univariate thresholding in linear regression and nearest shrunken centroids in classification. We call the procedure Cox univariate shrinkage and demonstrate its usefulness on real and simulated data. The method has the attractive property of being essentially univariate in its operation: the features are entered into the model based on the size of their Cox score statistics. We illustrate the new method on real and simulated data, and compare it to other proposed methods for survival prediction with a large number of predictors.

Suggested Citation

  • Tibshirani Robert J., 2009. "Univariate Shrinkage in the Cox Model for High Dimensional Data," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 8(1), pages 1-18, April.
  • Handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:21

    Download full text from publisher

    File URL:
    Download Restriction: For access to full text, subscription to the journal or payment for the individual article is required.

    As the access to this document is restricted, you may want to search for a different version of it.


    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.

    Cited by:

    1. Chakraborty, Sounak & Guo, Ruixin, 2011. "A Bayesian hybrid Huberized support vector machine and its applications in high-dimensional medical data," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1342-1356, March.
    2. Zhao, Sihai Dave & Li, Yi, 2012. "Principled sure independence screening for Cox models with ultra-high-dimensional covariates," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 397-411.
    3. repec:eee:csdana:v:112:y:2017:i:c:p:1-13 is not listed on IDEAS
    4. repec:eee:csdana:v:119:y:2018:i:c:p:74-85 is not listed on IDEAS
    5. Emura, Takeshi & Chen, Yi-Hau & Chen, Hsuan-Yu, 2012. "Survival prediction based on compound covariate under cox proportional hazard models," MPRA Paper 41149, University Library of Munich, Germany.

    More about this item


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:21. 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: (Peter Golla). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.