IDEAS home Printed from
   My bibliography  Save this article

Variable screening in predicting clinical outcome with high-dimensional microarrays


  • Shao, Jun
  • Chow, Shein-Chung


Statistical modeling is an important area of biomarker research of important genes for new drug targets, drug candidate validation, disease diagnoses, personalized treatment, and prediction of clinical outcome of a treatment. A widely adopted technology is the use of microarray data that are typically very high dimensional. After screening chromosomes for relative genes using methods such as quantitative trait locus mapping, there may still be a few thousands of genes related to the clinical outcome of interest. On the other hand, the sample size (the number of subjects) in a clinical study is typically much smaller. Under the assumption that only a few important genes are actually related to the clinical outcome, we propose a variable screening procedure to eliminate genes having negligible effects on the clinical outcome. Once the dimension of microarray data is reduced to a manageable number relative to the sample size, one can select a final set of genes via a well-known variable selection method such as the cross-validation. We establish the asymptotic consistency of the proposed variable screening procedure. Some simulation results are also presented.

Suggested Citation

  • Shao, Jun & Chow, Shein-Chung, 2007. "Variable screening in predicting clinical outcome with high-dimensional microarrays," Journal of Multivariate Analysis, Elsevier, vol. 98(8), pages 1529-1538, September.
  • Handle: RePEc:eee:jmvana:v:98:y:2007:i:8:p:1529-1538

    Download full text from publisher

    File URL:
    Download Restriction: Full text for ScienceDirect subscribers only

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

    References listed on IDEAS

    1. J. C. van Houwelingen, 2001. "Shrinkage and Penalized Likelihood as Methods to Improve Predictive Accuracy," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 55(1), pages 17-34.
    Full references (including those not matched with items on IDEAS)


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

    Cited by:

    1. Luo, June & Kulasekera, K.B., 2013. "Error covariance matrix estimation using ridge estimator," Statistics & Probability Letters, Elsevier, vol. 83(1), pages 257-264.
    2. Luo, June, 2010. "The discovery of mean square error consistency of a ridge estimator," Statistics & Probability Letters, Elsevier, vol. 80(5-6), pages 343-347, March.
    3. Luo, June, 2012. "Asymptotic efficiency of ridge estimator in linear and semiparametric linear models," Statistics & Probability Letters, Elsevier, vol. 82(1), pages 58-62.


    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:eee:jmvana:v:98:y:2007:i:8:p:1529-1538. 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: (Dana Niculescu). 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.

    If CitEc recognized a reference but did not link an item in RePEc to it, you can help with 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.