IDEAS home Printed from https://ideas.repec.org/a/spr/metrik/v59y2004i3p245-261.html
   My bibliography  Save this article

Strong uniform convergence of the recursive regression estimator under φ-mixing conditions

Author

Listed:
  • Li Wang
  • Han-Ying Liang

Abstract

Suppose the observations (X i , Y i ) taking values in R d ×R, [InlineMediaObject not available: see fulltext.] are φ-mixing. Compared with the i.i.d. case, some known strong uniform convergence results for the estimators of the regression function r(x)=E(Y i |X i =x) need strong moment conditions under φ-mixing setting. We consider the following improved kernel estimators of r(x) suggested by Cheng (1983): [InlineMediaObject not available: see fulltext.] Qian and Mammitzsch (2000) investigated the strong uniform convergence and convergence rate for [InlineMediaObject not available: see fulltext.] to r(x) under weaker moment conditions than those of the others in the literature, and the optimal convergence rate can be attained under almost the same conditions as stated in Theorem 3.3.2 of Györfi et al. (1989). In this paper, under the similar conditions of Qian and Mammitzsch (2000), we study the strong uniform convergence and convergence rates for [InlineMediaObject not available: see fulltext.] (j=2,3) to r(x), which have not been discussed by Qian and Mammitzsch (2000). In contrast to [InlineMediaObject not available: see fulltext.], our estimators [InlineMediaObject not available: see fulltext.] and [InlineMediaObject not available: see fulltext.] are recursive, which is highly desirable for practical computation. Copyright Springer-Verlag 2004

Suggested Citation

  • Li Wang & Han-Ying Liang, 2004. "Strong uniform convergence of the recursive regression estimator under φ-mixing conditions," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 59(3), pages 245-261, June.
  • Handle: RePEc:spr:metrik:v:59:y:2004:i:3:p:245-261
    DOI: 10.1007/s001840300282
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1007/s001840300282
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1007/s001840300282?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. Aboubacar Amiri, 2013. "Asymptotic normality of recursive estimators under strong mixing conditions," Statistical Inference for Stochastic Processes, Springer, vol. 16(2), pages 81-96, July.

    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:spr:metrik:v:59:y:2004:i:3:p:245-261. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.