IDEAS home Printed from https://ideas.repec.org/a/bla/jorssb/v62y2000i3p431-448.html
   My bibliography  Save this article

On variable bandwidth selection in local polynomial regression

Author

Listed:
  • Kjell Doksum
  • Derick Peterson
  • Alex Samarov

Abstract

The performances of data‐driven bandwidth selection procedures in local polynomial regression are investigated by using asymptotic methods and simulation. The bandwidth selection procedures considered are based on minimizing ‘prelimit’ approximations to the (conditional) mean‐squared error (MSE) when the MSE is considered as a function of the bandwidth h. We first consider approximations to the MSE that are based on Taylor expansions around h=0 of the bias part of the MSE. These approximations lead to estimators of the MSE that are accurate only for small bandwidths h. We also consider a bias estimator which instead of using small h approximations to bias naïvely estimates bias as the difference of two local polynomial estimators of different order and we show that this estimator performs well only for moderate to large h. We next define a hybrid bias estimator which equals the Taylor‐expansion‐based estimator for small h and the difference estimator for moderate to large h. We find that the MSE estimator based on this hybrid bias estimator leads to a bandwidth selection procedure with good asymptotic and, for our Monte Carlo examples, finite sample properties.

Suggested Citation

  • Kjell Doksum & Derick Peterson & Alex Samarov, 2000. "On variable bandwidth selection in local polynomial regression," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(3), pages 431-448.
  • Handle: RePEc:bla:jorssb:v:62:y:2000:i:3:p:431-448
    DOI: 10.1111/1467-9868.00242
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/1467-9868.00242
    Download Restriction: no

    File URL: https://libkey.io/10.1111/1467-9868.00242?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. Ichimura, Hidehiko & Todd, Petra E., 2007. "Implementing Nonparametric and Semiparametric Estimators," Handbook of Econometrics, in: J.J. Heckman & E.E. Leamer (ed.), Handbook of Econometrics, edition 1, volume 6, chapter 74, Elsevier.
    2. Lakhal-Chaieb Lajmi & Greenwood Celia M.T. & Ouhourane Mohamed & Zhao Kaiqiong & Abdous Belkacem & Oualkacha Karim, 2017. "A smoothed EM-algorithm for DNA methylation profiles from sequencing-based methods in cell lines or for a single cell type," Statistical Applications in Genetics and Molecular Biology, De Gruyter, vol. 16(5-6), pages 313-331, December.
    3. Derick R. Peterson & Hongwei Zhao & Sara Eapen, 2003. "Using Local Correlation in Kernel-Based Smoothers for Dependent Data," Biometrics, The International Biometric Society, vol. 59(4), pages 984-991, December.

    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:jorssb:v:62:y:2000:i:3:p:431-448. 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: https://edirc.repec.org/data/rssssea.html .

    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.