Advanced Search
MyIDEAS: Login to save this article or follow this journal

A Constrained â„“1 Minimization Approach to Sparse Precision Matrix Estimation


Author Info

  • Cai, Tony
  • Liu, Weidong
  • Luo, Xi
Registered author(s):


    No abstract is available for this item.

    Download Info

    If 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.
    File URL:
    File Function: full text
    Download Restriction: Access to full text is restricted to subscribers.

    As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.

    Bibliographic Info

    Article provided by American Statistical Association in its journal Journal of the American Statistical Association.

    Volume (Year): 106 (2011)
    Issue (Month): 494 ()
    Pages: 594-607

    as in new window
    Handle: RePEc:bes:jnlasa:v:106:i:494:y:2011:p:594-607

    Contact details of provider:
    Web page:

    Order Information:

    Related research



    No references listed on IDEAS
    You can help add them by filling out this form.


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

    Cited by:
    1. Yin, Jianxin & Li, Hongzhe, 2013. "Adjusting for high-dimensional covariates in sparse precision matrix estimation by ℓ1-penalization," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 365-381.
    2. Taras Bodnar & Arjun K. Gupta & Nestor Parolya, 2013. "Optimal Linear Shrinkage Estimator for Large Dimensional Precision Matrix," Papers 1308.0931,, revised Mar 2014.
    3. Vahe Avagyan & Andrés M. Alonso & Francisco J. Nogales, 2014. "Improving the graphical lasso estimation for the precision matrix through roots ot the sample convariance matrix," Statistics and Econometrics Working Papers ws141208, Universidad Carlos III, Departamento de Estadística y Econometría.
    4. Luo, Shan & Chen, Zehua, 2014. "Edge detection in sparse Gaussian graphical models," Computational Statistics & Data Analysis, Elsevier, vol. 70(C), pages 138-152.
    5. Natalia Bailey & Vanessa Smith & Hashem Pesaran, 2014. "A multiple testing approach to the regularisation of large sample correlation matrices," Cambridge Working Papers in Economics 1413, Faculty of Economics, University of Cambridge.
    6. Natalia Bailey & M. Hashem Pesaran & L. Vanessa Smith, 2014. "A Multiple Testing Approach to the Regularisation of Large Sample Correlation Matrices," CESifo Working Paper Series 4834, CESifo Group Munich.


    This item is not listed on Wikipedia, on a reading list or among the top items on IDEAS.


    Access and download statistics


    When requesting a correction, please mention this item's handle: RePEc:bes:jnlasa:v:106:i:494:y:2011:p:594-607. 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: (Christopher F. Baum).

    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 references are entirely missing, you can add them using this form.

    If the full references list an item that is present in RePEc, but the system did not link 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 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.