IDEAS home Printed from
   My bibliography  Save this article

Efficient Variable Screening for Multivariate Analysis


  • Duarte Silva, António Pedro


It is shown how known algorithms for the comparison of all variables subsets in regression analysis can be adapted to subset comparisons in multivariate analysis, according to any index based on Wilks, Lawley-Hotelling, or Bartllet-Pillai statistics and, in some special cases, according to any function of the sample squared canonical correlations. The issues regarding the choice of an appropriate comparison criterion are discussed. The computational effort of the proposed algorithms is studied, and it is argued that, for a moderate number of variables, they should be preferred to stepwise selection methods. A software implementation of the methods discussed is freely available and can be downloaded from the Internet.

Suggested Citation

  • Duarte Silva, António Pedro, 2001. "Efficient Variable Screening for Multivariate Analysis," Journal of Multivariate Analysis, Elsevier, vol. 76(1), pages 35-62, January.
  • Handle: RePEc:eee:jmvana:v:76:y:2001:i:1:p:35-62

    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. Elliot Cramer & W. Nicewander, 1979. "Some symmetric, invariant measures of multivariate association," Psychometrika, Springer;The Psychometric Society, vol. 44(1), pages 43-54, March.
    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. Brusco, Michael J. & Steinley, Douglas, 2011. "Exact and approximate algorithms for variable selection in linear discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 123-131, January.
    2. du Jardin, Philippe & Séverin, Eric, 2012. "Forecasting financial failure using a Kohonen map: A comparative study to improve model stability over time," European Journal of Operational Research, Elsevier, vol. 221(2), pages 378-396.
    3. Michael Brusco & Renu Singh & Douglas Steinley, 2009. "Variable Neighborhood Search Heuristics for Selecting a Subset of Variables in Principal Component Analysis," Psychometrika, Springer;The Psychometric Society, vol. 74(4), pages 705-726, December.
    4. Cadima, Jorge & Cerdeira, J. Orestes & Minhoto, Manuel, 2004. "Computational aspects of algorithms for variable selection in the context of principal components," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 225-236, September.
    5. Brusco, Michael J., 2014. "A comparison of simulated annealing algorithms for variable selection in principal component analysis and discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 38-53.
    6. Nkiet, Guy Martial, 2012. "Direct variable selection for discrimination among several groups," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 151-163.
    7. Pedro Duarte Silva, A., 2011. "Two-group classification with high-dimensional correlated data: A factor model approach," Computational Statistics & Data Analysis, Elsevier, vol. 55(11), pages 2975-2990, November.
    8. A. Pedro Duarte Silva, 2009. "Exact and heuristic algorithms for variable selection: Extended Leaps and Bounds," Working Papers de Economia (Economics Working Papers) 01, Católica Porto Business School, Universidade Católica Portuguesa.
    9. repec:eee:ejores:v:261:y:2017:i:2:p:772-788 is not listed on IDEAS


    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:76:y:2001:i:1:p:35-62. 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.