IDEAS home Printed from https://ideas.repec.org/a/eee/stapro/v66y2004i2p161-169.html
   My bibliography  Save this article

Modified Nel and Van der Merwe test for the multivariate Behrens-Fisher problem

Author

Listed:
  • Krishnamoorthy, K.
  • Yu, Jianqi

Abstract

A new test to the multivariate Behrens-Fisher problem is obtained by modifying Nel and Van der Merwe's (Comm. Statist. Theory Methods 15 (1986) 3719) test. The new test is affine invariant and it simplifies to the Welch's approximate solution to the univariate case. The merits of the new test and two existing invariant tests are evaluated using Monte Carlo method. Monte Carlo comparison shows that the new test is as powerful as the other two methods while controlling the sizes satisfactorily.

Suggested Citation

  • Krishnamoorthy, K. & Yu, Jianqi, 2004. "Modified Nel and Van der Merwe test for the multivariate Behrens-Fisher problem," Statistics & Probability Letters, Elsevier, vol. 66(2), pages 161-169, January.
    • Handle: RePEc:eee:stapro:v:66:y:2004:i:2:p:161-169
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-7152(03)00329-8
    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.

    Citations

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


    Cited by:

    1. Ivan Zezula, 2009. "Implementation of a new solution to the multivariate Behrens-Fisher problem," Stata Journal, StataCorp LP, vol. 9(4), pages 593-598, December.
    2. Stephanie Aerts & Gentiane Haesbroeck, 2017. "Robust asymptotic tests for the equality of multivariate coefficients of variation," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(1), pages 163-187, March.
    3. Jin-Ting Zhang & Xuefeng Liu, 2013. "A modified Bartlett test for heteroscedastic one-way MANOVA," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 76(1), pages 135-152, January.
    4. S. H. Lin & R. S. Wang, 2009. "Inferences on a linear combination of K multivariate normal mean vectors," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(4), pages 415-428.
    5. Tzviel Frostig & Yoav Benjamini, 2022. "Testing the equality of multivariate means when $$p>n$$ p > n by combining the Hotelling and Simes tests," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 31(2), pages 390-415, June.
    6. Gamage, Jinadasa & Mathew, Thomas, 2008. "Inference on mean sub-vectors of two multivariate normal populations with unequal covariance matrices," Statistics & Probability Letters, Elsevier, vol. 78(4), pages 420-425, March.
    7. Zhang, Jin-Ting & Xiao, Shengning, 2012. "A note on the modified two-way MANOVA tests," Statistics & Probability Letters, Elsevier, vol. 82(3), pages 519-527.
    8. Xu, Li-Wen, 2015. "Parametric bootstrap approaches for two-way MANOVA with unequal cell sizes and unequal cell covariance matrices," Journal of Multivariate Analysis, Elsevier, vol. 133(C), pages 291-303.
    9. Masucci, Monica & Parker, Simon C. & Brusoni, Stefano & Camerani, Roberto, 2021. "How are corporate ventures evaluated and selected?," Technovation, Elsevier, vol. 99(C).
    10. Konietschke, Frank & Bathke, Arne C. & Harrar, Solomon W. & Pauly, Markus, 2015. "Parametric and nonparametric bootstrap methods for general MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 140(C), pages 291-301.
    11. Krishnamoorthy, K. & Yu, Jianqi, 2012. "Multivariate Behrens–Fisher problem with missing data," Journal of Multivariate Analysis, Elsevier, vol. 105(1), pages 141-150.
    12. Tsagris, Michail & Preston, Simon & T.A. Wood, Andrew, 2016. "Nonparametric hypothesis testing for equality of means on the simplex," MPRA Paper 72771, University Library of Munich, Germany.
    13. Harrar, Solomon W. & Kong, Xiaoli, 2022. "Recent developments in high-dimensional inference for multivariate data: Parametric, semiparametric and nonparametric approaches," Journal of Multivariate Analysis, Elsevier, vol. 188(C).

    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:66:y:2004:i:2:p:161-169. 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: 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.