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

Selection of components and degrees of smoothing via lasso in high dimensional nonparametric additive models

Contents:

Author Info

  • Zheng, Shurong
Registered author(s):

    Abstract

    This paper proposes a procedure for selecting components and degrees of smoothing in high dimensional nonparametric additive models. In the procedure, different components have different penalties, and all the smoothing parameters in one component have the same penalties. The idea is similar to, but in fact different from, Wang et al.'s [Wang, H., Li, G.D., Tsai, C.L., 2007. Regression coefficient and autoregressive order shrinkage and selection via the lasso. Journal of the Royal Statistical Society, Series B 69, 63-78] modified lasso, which requires different penalties for different parameters. The procedure obtains the sequence of components according to the importance of these components by Efron et al.'s [Efron, B., Hastie, T., Johnstone, I., Tibshirani, R., 2004. Least angle regression. Annals of Statistics 32, 407-489] LARS. CV or BIC selector can be used to select the tuning parameters in the procedure, where some asymptotic properties are proved. Some simulation results and two examples are used to illustrate the procedure.

    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: http://www.sciencedirect.com/science/article/B6V8V-4SXYFVD-1/2/18693a4c588cc0290fe164f5e1fa578f
    Download Restriction: Full text for ScienceDirect subscribers only.

    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 Elsevier in its journal Computational Statistics & Data Analysis.

    Volume (Year): 53 (2008)
    Issue (Month): 1 (September)
    Pages: 164-175

    as in new window
    Handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:164-175

    Contact details of provider:
    Web page: http://www.elsevier.com/locate/csda

    Related research

    Keywords:

    References

    References listed on IDEAS
    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.:
    as in new window
    1. Hansheng Wang & Guodong Li & Chih-Ling Tsai, 2007. "Regression coefficient and autoregressive order shrinkage and selection via the lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 69(1), pages 63-78.
    2. Simon N. Wood, 2004. "Stable and Efficient Multiple Smoothing Parameter Estimation for Generalized Additive Models," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 673-686, January.
    3. Ferraty, F. & Vieu, P., 2003. "Curves discrimination: a nonparametric functional approach," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 161-173, October.
    Full references (including those not matched with items on IDEAS)

    Citations

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

    Cited by:
    1. Boj, Eva & Delicado, Pedro & Fortiana, Josep, 2010. "Distance-based local linear regression for functional predictors," Computational Statistics & Data Analysis, Elsevier, vol. 54(2), pages 429-437, February.
    2. de Uña Álvarez, Jacobo & Roca Pardiñas, Javier, 2009. "Additive models in censored regression," Computational Statistics & Data Analysis, Elsevier, vol. 53(9), pages 3490-3501, July.

    Lists

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

    Statistics

    Access and download statistics

    Corrections

    When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:53:y:2008:i:1:p:164-175. 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: (Zhang, Lei).

    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.