IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v115y2020i531p1227-1235.html
   My bibliography  Save this article

Feature Selection by Canonical Correlation Search in High-Dimensional Multiresponse Models With Complex Group Structures

Author

Listed:
  • Shan Luo
  • Zehua Chen

Abstract

High-dimensional multiresponse models with complex group structures in both the response variables and the covariates arise from current researches in important fields such as genetics and medicine. However, no enough research has been done on such models. One of a few researches, if not the only one, is the article by Li, Nan, and Zhu where the sparse group Lasso approach is extended to such models. In this article, we propose a novel approach named the sequential canonical correlation search (SCCS) procedure. In the SCCS procedure, the nonzero group by group blocks of regression coefficients are searched stepwise using a canonical correlation measure. Each step of the procedure consists of a block selection and a sparsity identification. The model selection criterion, EBIC, is used as the stopping rule of the procedure. We establish the selection consistency of the SCCS procedure and conduct simulation studies for the comparison of existing methods. The SCCS procedure has two advantages over the sparse grouped Lasso method: (i) it is more accurate in the identification of nonzero coefficient blocks and their nonzero entries, and (ii) its implementation is not limited by the dimensionality of the models and requires much less computation. A real example in genetic studies is also considered. Supplementary materials for this article are available online.

Suggested Citation

  • Shan Luo & Zehua Chen, 2020. "Feature Selection by Canonical Correlation Search in High-Dimensional Multiresponse Models With Complex Group Structures," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 115(531), pages 1227-1235, July.
  • Handle: RePEc:taf:jnlasa:v:115:y:2020:i:531:p:1227-1235
    DOI: 10.1080/01621459.2019.1609972
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2019.1609972
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2019.1609972?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

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

    Citations

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


    Cited by:

    1. Jun Lu & Dan Wang & Qinqin Hu, 2022. "Interaction screening via canonical correlation," Computational Statistics, Springer, vol. 37(5), pages 2637-2670, November.
    2. Ping Wang & Lu Lin, 2023. "Conditional characteristic feature screening for massive imbalanced data," Statistical Papers, Springer, vol. 64(3), pages 807-834, June.
    3. Yuanyuan Shi & Junyu Zhao & Xianchong Song & Zuoyu Qin & Lichao Wu & Huili Wang & Jian Tang, 2021. "Hyperspectral band selection and modeling of soil organic matter content in a forest using the Ranger algorithm," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-15, June.
    4. Juan C. Laria & M. Carmen Aguilera-Morillo & Rosa E. Lillo, 2023. "Group linear algorithm with sparse principal decomposition: a variable selection and clustering method for generalized linear models," Statistical Papers, Springer, vol. 64(1), pages 227-253, February.

    More about this item

    Statistics

    Access and download statistics

    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:taf:jnlasa:v:115:y:2020:i:531:p:1227-1235. See general information about how to correct material in RePEc.

    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.

    We have no bibliographic references for this item. You can help adding them by using 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.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.