IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v155y2019ic6.html
   My bibliography  Save this article

Convergence rate of Krasulina estimator

Author

Listed:
  • Chen, Jiangning

Abstract

Principal component analysis (PCA) is one of the most commonly used statistical procedures with a wide range of applications. Consider the points X1,X2,…,Xn are vectors drawn i.i.d. from a distribution with mean zero and covariance Σ, where Σ is unknown. Let An=XnXnT, then E[An]=Σ. This paper considers the problem of finding the smallest eigenvalue and eigenvector of matrix Σ. A classical estimator of this type is due to (Krasulina, 1969). We are going to state the convergence proof of Krasulina for the smallest eigenvalue and corresponding eigenvector, and then find their convergence rate.

Suggested Citation

  • Chen, Jiangning, 2019. "Convergence rate of Krasulina estimator," Statistics & Probability Letters, Elsevier, vol. 155(C), pages 1-1.
    • Handle: RePEc:eee:stapro:v:155:y:2019:i:c:6
    DOI: 10.1016/j.spl.2019.108562
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167715219302081
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.spl.2019.108562?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.

    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:stapro:v:155:y:2019:i:c:6. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.