IDEAS home Printed from https://ideas.repec.org/a/bla/jtsera/v39y2018i2p212-238.html
   My bibliography  Save this article

Square†Root LASSO for High†Dimensional Sparse Linear Systems with Weakly Dependent Errors

Author

Listed:
  • Fang Xie
  • Zhijie Xiao

Abstract

We study the square†root LASSO method for high†dimensional sparse linear models with weakly dependent errors. The asymptotic and non†asymptotic bounds for the estimation errors are derived. Our results cover a wide range of weakly dependent errors, including α†mixing, Ï â€ mixing, ϕ†mixing, and m†dependent types. Numerical simulations are conducted to show the consistency property of square†root LASSO. An empirical application to financial data highlights the importance of the results and method.

Suggested Citation

  • Fang Xie & Zhijie Xiao, 2018. "Square†Root LASSO for High†Dimensional Sparse Linear Systems with Weakly Dependent Errors," Journal of Time Series Analysis, Wiley Blackwell, vol. 39(2), pages 212-238, March.
    • Handle: RePEc:bla:jtsera:v:39:y:2018:i:2:p:212-238
    DOI: 10.1111/jtsa.12278
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/jtsa.12278
    Download Restriction: no
    File URL: https://libkey.io/10.1111/jtsa.12278?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
    ---><---

    Citations

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


    Cited by:

    1. Panxu Yuan & Xiao Guo, 2022. "High-dimensional inference for linear model with correlated errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 85(1), pages 21-52, January.
    2. Ling Peng & Yan Zhu & Wenxuan Zhong, 2023. "Lasso regression in sparse linear model with $$\varphi $$ φ -mixing errors," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 86(1), pages 1-26, January.

    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:bla:jtsera:v:39:y:2018:i:2:p:212-238. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www.blackwellpublishing.com/journal.asp?ref=0143-9782 .

    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.