IDEAS home Printed from https://ideas.repec.org/a/spr/metron/v82y2024i2d10.1007_s40300-023-00253-4.html
   My bibliography  Save this article

Generalized regression estimators with concave penalties and a comparison to lasso type estimators

Author

Listed:
  • Elena McDonald

    (Archer Daniels Midland)

  • Xin Wang

    (San Diego State University)

Abstract

The generalized regression (GREG) estimator is usually used in survey sampling when incorporating auxiliary information. Generally, not all available covariates significantly contribute to the estimation process when there are multiple covariates. We propose two new GREG estimators based on concave penalties: one built from the smoothly clipped absolute deviation (SCAD) penalty and the other built from the minimax concave penalty (MCP). The performances of these estimators are compared to lasso-type estimators through a simulation study in a simple random sample (SRS) setting and a probability proportional to size (PPS) sample setting. It is shown that the proposed estimators produce improved estimates of the population total compared to that of the traditional GREG estimator and the estimators built from LASSO. Asymptotic properties are also derived for the proposed estimators. Bootstrap methods are also explored to improve coverage probability when the sample size is small.

Suggested Citation

  • 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.
    • Handle: RePEc:spr:metron:v:82:y:2024:i:2:d:10.1007_s40300-023-00253-4
    DOI: 10.1007/s40300-023-00253-4
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40300-023-00253-4
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40300-023-00253-4?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:spr:metron:v:82:y:2024:i:2:d:10.1007_s40300-023-00253-4. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    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.