IDEAS home Printed from https://ideas.repec.org/a/bla/jorssa/v167y2004i2p323-339.html
   My bibliography  Save this article

Robust automatic methods for outlier and error detection

Author

Listed:
  • Ray Chambers
  • Adão Hentges
  • Xinqiang Zhao

Abstract

Summary. Editing in surveys of economic populations is often complicated by the fact that outliers due to errors in the data are mixed in with correct, but extreme, data values. We describe and evaluate two automatic techniques for the identification of errors in such long‐tailed data distributions. The first is a forward search procedure based on finding a sequence of error‐free subsets of the error‐contaminated data and then using regression modelling within these subsets to identify errors. The second uses a robust regression tree modelling procedure to identify errors. Both approaches can be implemented on a univariate basis or on a multivariate basis. An application to a business survey data set that contains a mix of extreme errors and true outliers is described.

Suggested Citation

  • Ray Chambers & Adão Hentges & Xinqiang Zhao, 2004. "Robust automatic methods for outlier and error detection," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 167(2), pages 323-339, May.
    • Handle: RePEc:bla:jorssa:v:167:y:2004:i:2:p:323-339
    DOI: 10.1111/j.1467-985X.2004.00748.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1467-985X.2004.00748.x
    Download Restriction: no
    File URL: https://libkey.io/10.1111/j.1467-985X.2004.00748.x?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. Robert Graham Clark & Philip Kokic & Paul A. Smith, 2017. "A Comparison of two Robust Estimation Methods for Business Surveys," International Statistical Review, International Statistical Institute, vol. 85(2), pages 270-289, August.
    2. Sullivan, Joe H. & Warkentin, Merrill & Wallace, Linda, 2021. "So many ways for assessing outliers: What really works and does it matter?," Journal of Business Research, Elsevier, vol. 132(C), pages 530-543.

    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:jorssa:v:167:y:2004:i:2:p:323-339. 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.