IDEAS home Printed from https://ideas.repec.org/a/taf/jnlasa/v116y2021i535p1413-1427.html
   My bibliography  Save this article

Metropolized Knockoff Sampling

Author

Listed:
  • Stephen Bates
  • Emmanuel Candès
  • Lucas Janson
  • Wenshuo Wang

Abstract

Model-X knockoffs is a wrapper that transforms essentially any feature importance measure into a variable selection algorithm, which discovers true effects while rigorously controlling the expected fraction of false positives. A frequently discussed challenge to apply this method is to construct knockoff variables, which are synthetic variables obeying a crucial exchangeability property with the explanatory variables under study. This article introduces techniques for knockoff generation in great generality: we provide a sequential characterization of all possible knockoff distributions, which leads to a Metropolis–Hastings formulation of an exact knockoff sampler. We further show how to use conditional independence structure to speed up computations. Combining these two threads, we introduce an explicit set of sequential algorithms and empirically demonstrate their effectiveness. Our theoretical analysis proves that our algorithms achieve near-optimal computational complexity in certain cases. The techniques we develop are sufficiently rich to enable knockoff sampling in challenging models including cases where the covariates are continuous and heavy-tailed, and follow a graphical model such as the Ising model. Supplementary materials for this article are available online.

Suggested Citation

  • Stephen Bates & Emmanuel Candès & Lucas Janson & Wenshuo Wang, 2021. "Metropolized Knockoff Sampling," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 116(535), pages 1413-1427, July.
  • Handle: RePEc:taf:jnlasa:v:116:y:2021:i:535:p:1413-1427
    DOI: 10.1080/01621459.2020.1729163
    as

    Download full text from publisher

    File URL: http://hdl.handle.net/10.1080/01621459.2020.1729163
    Download Restriction: Access to full text is restricted to subscribers.

    File URL: https://libkey.io/10.1080/01621459.2020.1729163?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. Srinivasan, Arun & Xue, Lingzhou & Zhan, Xiang, 2023. "Identification of microbial features in multivariate regression under false discovery rate control," Computational Statistics & Data Analysis, Elsevier, vol. 181(C).
    2. Ran Dai & Cheng Zheng, 2023. "False discovery rate‐controlled multiple testing for union null hypotheses: a knockoff‐based approach," Biometrics, The International Biometric Society, vol. 79(4), pages 3497-3509, December.
    3. Panxu Yuan & Yinfei Kong & Gaorong Li, 2024. "FDR control and power analysis for high-dimensional logistic regression via StabKoff," Statistical Papers, Springer, vol. 65(5), pages 2719-2749, July.

    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:taf:jnlasa:v:116:y:2021:i:535:p:1413-1427. 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: Chris Longhurst (email available below). General contact details of provider: http://www.tandfonline.com/UASA20 .

    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.