IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v62y1997i2p169-180.html
   My bibliography  Save this article

Linear Discrimination with Adaptive Ridge Classification Rules

Author

Listed:
  • Loh, Wei-Liem

Abstract

This article considers the use of adaptive ridge classification rules for classifying an observation as coming from one of two multivariate normal distributionsN([mu](1), [Sigma]) andN([mu](2), [Sigma]). In particular, the asymptotic expected error rates for a general class of these rules are obtained and are compared with that of the usual linear discriminant rule.

Suggested Citation

  • Loh, Wei-Liem, 1997. "Linear Discrimination with Adaptive Ridge Classification Rules," Journal of Multivariate Analysis, Elsevier, vol. 62(2), pages 169-180, August.
  • Handle: RePEc:eee:jmvana:v:62:y:1997:i:2:p:169-180
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(97)91676-6
    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. T. Anderson, 1951. "Classification by multivariate analysis," Psychometrika, Springer;The Psychometric Society, vol. 16(1), pages 31-50, March.
    2. Loh, W. L., 1995. "On Linear Discriminant Analysis with Adaptive Ridge Classification Rules," Journal of Multivariate Analysis, Elsevier, vol. 53(2), pages 264-278, May.
    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. Kubokawa, Tatsuya & Srivastava, Muni S., 2008. "Estimation of the precision matrix of a singular Wishart distribution and its application in high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 99(9), pages 1906-1928, October.
    2. Raudys, Sarunas & Young, Dean M., 2004. "Results in statistical discriminant analysis: a review of the former Soviet Union literature," Journal of Multivariate Analysis, Elsevier, vol. 89(1), pages 1-35, April.

    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. Raudys, Sarunas & Young, Dean M., 2004. "Results in statistical discriminant analysis: a review of the former Soviet Union literature," Journal of Multivariate Analysis, Elsevier, vol. 89(1), pages 1-35, April.
    2. Richard Melton, 1963. "Some remarks on failure to meet assumptions in discriminant analyses," Psychometrika, Springer;The Psychometric Society, vol. 28(1), pages 49-53, March.
    3. Wakaki, Hirofumi & Aoshima, Makoto, 2009. "Optimal discriminant functions for normal populations," Journal of Multivariate Analysis, Elsevier, vol. 100(1), pages 58-69, January.
    4. Gray, H. L. & Baek, J. & Woodward, W. A. & Miller, J. & Fisk, M., 1996. "A bootstrap generalized likelihood ratio test in discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 22(2), pages 137-158, July.
    5. Hie-Choon Chung & Chien-Pai Han, 2000. "Discriminant Analysis When a Block of Observations is Missing," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 52(3), pages 544-556, September.
    6. N. Balakrishnan & M. Tiku, 1988. "Robust classification procedures based on dichotomous and continuous variables," Journal of Classification, Springer;The Classification Society, vol. 5(1), pages 53-80, March.
    7. Mkhadri, A. & Celeux, G. & Nasroallah, A., 1997. "Regularization in discriminant analysis: an overview," Computational Statistics & Data Analysis, Elsevier, vol. 23(3), pages 403-423, January.
    8. Pushkal Kumar & Manas Ranjan Tripathy & Somesh Kumar, 2023. "Bayesian estimation and classification for two logistic populations with a common location," Computational Statistics, Springer, vol. 38(2), pages 711-748, June.
    9. Chung, Hie-Choon & Han, Chien-Pai, 2009. "Conditional confidence intervals for classification error rate," Computational Statistics & Data Analysis, Elsevier, vol. 53(12), pages 4358-4369, October.

    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:jmvana:v:62:y:1997:i:2:p:169-180. 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.