IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v102y2015i3p589-599..html
   My bibliography  Save this article

Outlier detection for high-dimensional data

Author

Listed:
  • Kwangil Ro
  • Changliang Zou
  • Zhaojun Wang
  • Guosheng Yin

Abstract

Outlier detection is an integral component of statistical modelling and estimation. For high-dimensional data, classical methods based on the Mahalanobis distance are usually not applicable. We propose an outlier detection procedure that replaces the classical minimum covariance determinant estimator with a high-breakdown minimum diagonal product estimator. The cut-off value is obtained from the asymptotic distribution of the distance, which enables us to control the Type I error and deliver robust outlier detection. Simulation studies show that the proposed method behaves well for high-dimensional data.

Suggested Citation

  • Kwangil Ro & Changliang Zou & Zhaojun Wang & Guosheng Yin, 2015. "Outlier detection for high-dimensional data," Biometrika, Biometrika Trust, vol. 102(3), pages 589-599.
    • Handle: RePEc:oup:biomet:v:102:y:2015:i:3:p:589-599.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asv021
    Download Restriction: Access to full text is restricted to subscribers.
    ---><---

    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. Chung, Hee Cheol & Ahn, Jeongyoun, 2021. "Subspace rotations for high-dimensional outlier detection," Journal of Multivariate Analysis, Elsevier, vol. 183(C).
    2. Rubino, Michele & Vitolla, Filippo & Raimo, Nicola & Garzoni, Antonello, 2019. "Cultura nazionale e livello di digitalizzazione delle imprese europee: evidenze empiriche," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, pages 581-593.
    3. P. Navarro-Esteban & J. A. Cuesta-Albertos, 2021. "High-dimensional outlier detection using random projections," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 30(4), pages 908-934, December.
    4. Marc Chataigner & Stéphane Crépey & Jiang Pu, 2020. "Nowcasting Networks," Post-Print hal-03910123, HAL.
    5. Rodrigo Puentes & Carolina Marchant & Víctor Leiva & Jorge I. Figueroa-Zúñiga & Fabrizio Ruggeri, 2021. "Predicting PM2.5 and PM10 Levels during Critical Episodes Management in Santiago, Chile, with a Bivariate Birnbaum-Saunders Log-Linear Model," Mathematics, MDPI, vol. 9(6), pages 1-24, March.
    6. Jan Kalina & Jan Tichavský, 2022. "The minimum weighted covariance determinant estimator for high-dimensional data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 16(4), pages 977-999, December.
    7. Michail Tsagris, 2022. "The FEDHC Bayesian Network Learning Algorithm," Mathematics, MDPI, vol. 10(15), pages 1-28, July.
    8. Marc Chataigner & Stephane Crepey & Jiang Pu, 2020. "Nowcasting Networks," Papers 2011.13687, arXiv.org.

    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:oup:biomet:v:102:y:2015:i:3:p:589-599.. 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: Oxford University Press (email available below). General contact details of provider: https://academic.oup.com/biomet .

    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.