IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v207y2025ics0167947325000222.html
   My bibliography  Save this article

Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm

Author

Listed:
  • McLain, Alexander C.
  • Zgodic, Anja
  • Bondell, Howard

Abstract

Bayesian variable selection methods are powerful techniques for fitting sparse high-dimensional linear regression models. However, many are computationally intensive or require restrictive prior distributions on model parameters. A computationally efficient and powerful Bayesian approach is presented for sparse high-dimensional linear regression, requiring only minimal prior assumptions on parameters through plug-in empirical Bayes estimates of hyperparameters. The method employs a Parameter-Expanded Expectation-Conditional-Maximization (PX-ECM) algorithm to estimate maximum a posteriori (MAP) values of parameters via computationally efficient coordinate-wise optimization. The popular two-group approach to multiple testing motivates the E-step, resulting in a PaRtitiOned empirical Bayes Ecm (PROBE) algorithm for sparse high-dimensional linear regression. Both one-at-a-time and all-at-once optimization can be used to complete PROBE. Extensive simulation studies and analyses of cancer cell drug responses are conducted to compare PROBE's empirical properties with those of related methods. Implementation is available through the R package probe.

Suggested Citation

  • McLain, Alexander C. & Zgodic, Anja & Bondell, Howard, 2025. "Efficient sparse high-dimensional linear regression with a partitioned empirical Bayes ECM algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 207(C).
  • Handle: RePEc:eee:csdana:v:207:y:2025:i:c:s0167947325000222
    DOI: 10.1016/j.csda.2025.108146
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167947325000222
    Download Restriction: Full text for ScienceDirect subscribers only.

    File URL: https://libkey.io/10.1016/j.csda.2025.108146?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.

    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:eee:csdana:v:207:y:2025:i:c:s0167947325000222. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    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.