IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v8y2020i6p982-d372091.html
   My bibliography  Save this article

Unbiased Least-Squares Modelling

Author

Listed:
  • Marta Gatto

    (Department of Mathematics, “Tullio Levi Civita”, University of Padova, Via Trieste 63, 35131 Padova, Italy)

  • Fabio Marcuzzi

    (Department of Mathematics, “Tullio Levi Civita”, University of Padova, Via Trieste 63, 35131 Padova, Italy)

Abstract

In this paper we analyze the bias in a general linear least-squares parameter estimation problem, when it is caused by deterministic variables that have not been included in the model. We propose a method to substantially reduce this bias, under the hypothesis that some a-priori information on the magnitude of the modelled and unmodelled components of the model is known. We call this method Unbiased Least-Squares (ULS) parameter estimation and present here its essential properties and some numerical results on an applied example.

Suggested Citation

  • Marta Gatto & Fabio Marcuzzi, 2020. "Unbiased Least-Squares Modelling," Mathematics, MDPI, vol. 8(6), pages 1-19, June.
    • Handle: RePEc:gam:jmathe:v:8:y:2020:i:6:p:982-:d:372091
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/8/6/982/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/8/6/982/
    Download Restriction: no
    ---><---

    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:gam:jmathe:v:8:y:2020:i:6:p:982-:d:372091. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.