IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v92y2015icp40-52.html
   My bibliography  Save this article

Studies of the adaptive network-constrained linear regression and its application

Author

Listed:
  • Yang, Hu
  • Yi, Danhui

Abstract

The network-constrained criterion is one of the fundamental variable selection models for high-dimensional data with correlated features. It is distinguished from others in that it can select features and simultaneously encourage global smoothness of the coefficients over the network via penalizing the weighted sum of squares of the scaled difference of the coefficients between neighbor vertices. However, because more features were selected while it was applied for the process of analysis of the “China Stock Market Financial Database—Financial Ratios”, the so-called adaptive network-constrained criterion was proposed to tackle the problem via assigning various weights to the lasso penalty. Similar to the adaptive lasso, the proposed model enjoys consistency in variable selection if the weights have been given correctly in advance. The simulations show that the proposed model performed better than the other variable selection techniques mentioned in the paper with regards to model fitting; meanwhile, it selected fewer features than the network-constrained criterion. Furthermore, the mean value of the cross-validation likelihood and the number of selected features are tested to be accurate enough for practical applications.

Suggested Citation

  • Yang, Hu & Yi, Danhui, 2015. "Studies of the adaptive network-constrained linear regression and its application," Computational Statistics & Data Analysis, Elsevier, vol. 92(C), pages 40-52.
  • Handle: RePEc:eee:csdana:v:92:y:2015:i:c:p:40-52
    DOI: 10.1016/j.csda.2015.06.008
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947315001462
    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

    as
    1. Robert Tibshirani & Michael Saunders & Saharon Rosset & Ji Zhu & Keith Knight, 2005. "Sparsity and smoothness via the fused lasso," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(1), pages 91-108, February.
    2. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    3. Chen Xiang LIU & Mohamed El Hedi AROURI, 2008. "Stock craze: an empirical analysis of PER in Chinese equity market," Economics Bulletin, AccessEcon, vol. 14(1), pages 1-17.
    4. Lukas Meier & Sara Van De Geer & Peter Bühlmann, 2008. "The group lasso for logistic regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 70(1), pages 53-71, February.
    5. repec:ebl:ecbull:v:14:y:2008:i:1:p:1-17 is not listed on IDEAS
    6. She, Yiyuan, 2012. "An iterative algorithm for fitting nonconvex penalized generalized linear models with grouped predictors," Computational Statistics & Data Analysis, Elsevier, vol. 56(10), pages 2976-2990.
    7. Hui Zou & Trevor Hastie, 2005. "Addendum: Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(5), pages 768-768, November.
    8. Myron J. Gordon & Eli Shapiro, 1956. "Capital Equipment Analysis: The Required Rate of Profit," Management Science, INFORMS, vol. 3(1), pages 102-110, October.
    9. Hui Zou & Trevor Hastie, 2005. "Regularization and variable selection via the elastic net," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 67(2), pages 301-320, April.
    10. Fan J. & Li R., 2001. "Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1348-1360, December.
    Full references (including those not matched with items on IDEAS)

    Corrections

    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:csdana:v:92:y:2015:i:c:p:40-52. 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: (Haili He). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.