IDEAS home Printed from https://ideas.repec.org/a/eee/jmvana/v100y2009i3p518-532.html
   My bibliography  Save this article

A test for the mean vector with fewer observations than the dimension under non-normality

Author

Listed:
  • Srivastava, Muni S.

Abstract

In this article, we consider the problem of testing that the mean vector in the model , where are random p-vectors, and zij are independently and identically distributed with finite four moments, ; that is need not be normally distributed. We shall assume that C is a pxp non-singular matrix, and there are fewer observations than the dimension, N

Suggested Citation

  • Srivastava, Muni S., 2009. "A test for the mean vector with fewer observations than the dimension under non-normality," Journal of Multivariate Analysis, Elsevier, vol. 100(3), pages 518-532, March.
    • Handle: RePEc:eee:jmvana:v:100:y:2009:i:3:p:518-532
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0047-259X(08)00152-8
    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.

    References listed on IDEAS

    as
    1. Srivastava, Muni S. & Du, Meng, 2008. "A test for the mean vector with fewer observations than the dimension," Journal of Multivariate Analysis, Elsevier, vol. 99(3), pages 386-402, March.
    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. Li, Yang & Wang, Zhaojun & Zou, Changliang, 2016. "A simpler spatial-sign-based two-sample test for high-dimensional data," Journal of Multivariate Analysis, Elsevier, vol. 149(C), pages 192-198.
    2. Lianjie Shu & Jinyu Fan, 2018. "A distribution‐free control chart for monitoring high‐dimensional processes based on interpoint distances," Naval Research Logistics (NRL), John Wiley & Sons, vol. 65(4), pages 317-330, June.
    3. 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.
    4. Saha, Enakshi & Sarkar, Soham & Ghosh, Anil K., 2017. "Some high-dimensional one-sample tests based on functions of interpoint distances," Journal of Multivariate Analysis, Elsevier, vol. 161(C), pages 83-95.
    5. Jiang Hu & Zhidong Bai & Chen Wang & Wei Wang, 2017. "On testing the equality of high dimensional mean vectors with unequal covariance matrices," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 69(2), pages 365-387, April.
    6. Feng, Long & Sun, Fasheng, 2015. "A note on high-dimensional two-sample test," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 29-36.
    7. Zhang, Jie & Pan, Meng, 2016. "A high-dimension two-sample test for the mean using cluster subspaces," Computational Statistics & Data Analysis, Elsevier, vol. 97(C), pages 87-97.
    8. Jamshid Namdari & Debashis Paul & Lili Wang, 2021. "High-Dimensional Linear Models: A Random Matrix Perspective," Sankhya A: The Indian Journal of Statistics, Springer;Indian Statistical Institute, vol. 83(2), pages 645-695, August.
    9. Dong, Kai & Pang, Herbert & Tong, Tiejun & Genton, Marc G., 2016. "Shrinkage-based diagonal Hotelling’s tests for high-dimensional small sample size data," Journal of Multivariate Analysis, Elsevier, vol. 143(C), pages 127-142.
    10. Schott, James R., 2008. "A test for independence of two sets of variables when the number of variables is large relative to the sample size," Statistics & Probability Letters, Elsevier, vol. 78(17), pages 3096-3102, December.
    11. Shen, Yanfeng & Lin, Zhengyan, 2015. "An adaptive test for the mean vector in large-p-small-n problems," Computational Statistics & Data Analysis, Elsevier, vol. 89(C), pages 25-38.
    12. Muni S. Srivastava & Tatsuya Kubokawa, 2011. "Tests for Multivariate Analysis of Variance in High Dimension Under Non-Normality," CIRJE F-Series CIRJE-F-831, CIRJE, Faculty of Economics, University of Tokyo.
    13. Zongliang Hu & Tiejun Tong & Marc G. Genton, 2019. "Diagonal likelihood ratio test for equality of mean vectors in high‐dimensional data," Biometrics, The International Biometric Society, vol. 75(1), pages 256-267, March.
    14. Yin, Yanqing, 2021. "Test for high-dimensional mean vector under missing observations," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    15. Feng, Long & Zhang, Xiaoxu & Liu, Binghui, 2020. "A high-dimensional spatial rank test for two-sample location problems," Computational Statistics & Data Analysis, Elsevier, vol. 144(C).
    16. Li, Jun, 2023. "Finite sample t-tests for high-dimensional means," Journal of Multivariate Analysis, Elsevier, vol. 196(C).
    17. Anders Bredahl Kock & David Preinerstorfer, 2019. "Power in High‐Dimensional Testing Problems," Econometrica, Econometric Society, vol. 87(3), pages 1055-1069, May.
    18. Muni S. Srivastava & Hirokazu Yanagihara & Tatsuya Kubokawa, 2014. "Tests for Covariance Matrices in High Dimension with Less Sample Size," CIRJE F-Series CIRJE-F-933, CIRJE, Faculty of Economics, University of Tokyo.
    19. Cai, T. Tony & Xia, Yin, 2014. "High-dimensional sparse MANOVA," Journal of Multivariate Analysis, Elsevier, vol. 131(C), pages 174-196.
    20. Katayama, Shota & Kano, Yutaka & Srivastava, Muni S., 2013. "Asymptotic distributions of some test criteria for the mean vector with fewer observations than the dimension," Journal of Multivariate Analysis, Elsevier, vol. 116(C), pages 410-421.

    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:100:y:2009:i:3:p:518-532. 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.