IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v30y1996i2p133-138.html
   My bibliography  Save this article

Mean square error matrix comparison of some estimators in linear regressions with multicollinearity

Author

Listed:
  • Sarkar, Nityananda

Abstract

The ordinary least squares, the principal components regression and the ordinary ridge regression estimators are special cases of the r - k class estimator proposed by Baye and Parker (1984) for regression models with multicollinearity. We obtain necessary and sufficient conditions for the superiority of the r - k class estimator over each of these three estimators by the criterion of mean square error matrix. We also suggest tests to verify if these conditions are indeed satisfied.

Suggested Citation

  • Sarkar, Nityananda, 1996. "Mean square error matrix comparison of some estimators in linear regressions with multicollinearity," Statistics & Probability Letters, Elsevier, vol. 30(2), pages 133-138, October.
    • Handle: RePEc:eee:stapro:v:30:y:1996:i:2:p:133-138
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-7152(95)00211-1
    Download Restriction: Full text for ScienceDirect subscribers only
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. P.A. Bekker & H. Neudecker, 1989. "Albert's theorem applied to problems of efficiency and MSE superiority," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 43(3), pages 157-167, September.
    2. Nityananda Sarkar, 1989. "Comparisons among some estimators in misspecified linear models with multicollinearity," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 41(4), pages 717-724, December.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Özkale, M. Revan & KaçIranlar, Selahattin, 2007. "Superiority of the r-d class estimator over some estimators by the mean square error matrix criterion," Statistics & Probability Letters, Elsevier, vol. 77(4), pages 438-446, February.
    2. 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.
    3. Shalini Chandra & Gargi Tyagi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 27-52, March.
    4. Shalini Chandra & Nityananda Sarkar, 2015. "Comparison of the r - (k, d) Class Estimator with some Estimators for Multicollinearity under the Mahalanobis Loss Function," International Econometric Review (IER), Econometric Research Association, vol. 7(1), pages 1-12, April.
    5. Gülesen Üstündagˇ Şiray & Selahattin Kaçıranlar & Sadullah Sakallıoğlu, 2014. "r − k Class estimator in the linear regression model with correlated errors," Statistical Papers, Springer, vol. 55(2), pages 393-407, May.
    6. Deniz Inan, 2015. "Combining the Liu-type estimator and the principal component regression estimator," Statistical Papers, Springer, vol. 56(1), pages 147-156, February.
    7. Xinfeng Chang & Hu Yang, 2012. "Combining two-parameter and principal component regression estimators," Statistical Papers, Springer, vol. 53(3), pages 549-562, August.
    8. Chandra Shalini & Tyagi Gargi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 27-52, March.

    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. Shalini Chandra & Gargi Tyagi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Polish Statistical Association, vol. 18(1), pages 27-52, March.
    2. Chandra Shalini & Tyagi Gargi, 2017. "On the Performance of Some Biased Estimators in a Misspecified Model with Correlated Regressors," Statistics in Transition New Series, Statistics Poland, vol. 18(1), pages 27-52, March.

    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:stapro:v:30:y:1996:i:2:p:133-138. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description .

    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.