IDEAS home Printed from https://ideas.repec.org/a/gam/jstats/v3y2020i4p33-541d441213.html
   My bibliography  Save this article

A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model

Author

Listed:
  • Issam Dawoud

    (Department of Mathematics, Al-Aqsa University, Gaza 4051, Palestine)

  • B. M. Golam Kibria

    (Department of Mathematics and Statistics, Florida International University, Miami, FL 33199, USA)

Abstract

In a multiple linear regression model, the ordinary least squares estimator is inefficient when the multicollinearity problem exists. Many authors have proposed different estimators to overcome the multicollinearity problem for linear regression models. This paper introduces a new regression estimator, called the Dawoud–Kibria estimator, as an alternative to the ordinary least squares estimator. Theory and simulation results show that this estimator performs better than other regression estimators under some conditions, according to the mean squares error criterion. The real-life datasets are used to illustrate the findings of the paper.

Suggested Citation

  • Issam Dawoud & B. M. Golam Kibria, 2020. "A New Biased Estimator to Combat the Multicollinearity of the Gaussian Linear Regression Model," Stats, MDPI, vol. 3(4), pages 1-16, November.
    • Handle: RePEc:gam:jstats:v:3:y:2020:i:4:p:33-541:d:441213
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2571-905X/3/4/33/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2571-905X/3/4/33/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Roozbeh, Mahdi, 2018. "Optimal QR-based estimation in partially linear regression models with correlated errors using GCV criterion," Computational Statistics & Data Analysis, Elsevier, vol. 117(C), pages 45-61.
    2. Kristofer Månsson & B. M. Golam Kibria & Ghazi Shukur, 2015. "Performance of Some Weighted Liu Estimators for Logit Regression Model: An Application to Swedish Accident Data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 44(2), pages 363-375, January.
    3. Betül Kan & Özlem Alpu & Berna Yazıcı, 2013. "Robust ridge and robust Liu estimator for regression based on the LTS estimator," Journal of Applied Statistics, Taylor & Francis Journals, vol. 40(3), pages 644-655.
    4. Fikri Akdeniz & Mahdi Roozbeh, 2019. "Generalized difference-based weighted mixed almost unbiased ridge estimator in partially linear models," Statistical Papers, Springer, vol. 60(5), pages 1717-1739, October.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Mohammad Arashi & Mina Norouzirad & Mahdi Roozbeh & Naushad Mamode Khan, 2021. "A High-Dimensional Counterpart for the Ridge Estimator in Multicollinear Situations," Mathematics, MDPI, vol. 9(23), pages 1-11, November.
    2. Guoping Zeng & Sha Tao, 2023. "A Generalized Linear Transformation and Its Effects on Logistic Regression," Mathematics, MDPI, vol. 11(2), pages 1-19, January.
    3. M Arashi & M Roozbeh & N A Hamzah & M Gasparini, 2021. "Ridge regression and its applications in genetic studies," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-17, April.
    4. M. Nooi Asl & H. Bevrani & R. Arabi Belaghi & K. Mansson, 2021. "Ridge-type shrinkage estimators in generalized linear models with an application to prostate cancer data," Statistical Papers, Springer, vol. 62(2), pages 1043-1085, April.
    5. Lei Qiao & Bing Wang, 2024. "Kernel-Based Multivariate Nonparametric CUSUM Multi-Chart for Detection of Abrupt Changes," Mathematics, MDPI, vol. 12(10), pages 1-12, May.
    6. M. Norouzirad & M. Arashi, 2019. "Preliminary test and Stein-type shrinkage ridge estimators in robust regression," Statistical Papers, Springer, vol. 60(6), pages 1849-1882, December.

    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:jstats:v:3:y:2020:i:4:p:33-541:d:441213. 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.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with 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.