IDEAS home Printed from https://ideas.repec.org/a/oup/biomet/v105y2018i1p31-44..html
   My bibliography  Save this article

Robust and consistent variable selection in high-dimensional generalized linear models

Author

Listed:
  • Marco Avella-Medina
  • Elvezio Ronchetti

Abstract

Summary Generalized linear models are popular for modelling a large variety of data. We consider variable selection through penalized methods by focusing on resistance issues in the presence of outlying data and other deviations from assumptions. We highlight the weaknesses of widely-used penalized M-estimators, propose a robust penalized quasilikelihood estimator, and show that it enjoys oracle properties in high dimensions and is stable in a neighbourhood of the model. We illustrate its finite-sample performance on simulated and real data.

Suggested Citation

  • Marco Avella-Medina & Elvezio Ronchetti, 2018. "Robust and consistent variable selection in high-dimensional generalized linear models," Biometrika, Biometrika Trust, vol. 105(1), pages 31-44.
    • Handle: RePEc:oup:biomet:v:105:y:2018:i:1:p:31-44.
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1093/biomet/asx070
    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. Elvezio Ronchetti, 2021. "The main contributions of robust statistics to statistical science and a new challenge," METRON, Springer;Sapienza Università di Roma, vol. 79(2), pages 127-135, August.
    2. Elena McDonald & Xin Wang, 2024. "Generalized regression estimators with concave penalties and a comparison to lasso type estimators," METRON, Springer;Sapienza Università di Roma, vol. 82(2), pages 213-239, August.
    3. Ana M. Bianco & Graciela Boente & Gonzalo Chebi, 2022. "Penalized robust estimators in sparse logistic regression," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(3), pages 563-594, September.
    4. Dries Cornilly & Lise Tubex & Stefan Van Aelst & Tim Verdonck, 2024. "Robust and sparse logistic regression," 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. 18(3), pages 663-679, September.

    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:105:y:2018:i:1:p:31-44.. 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.