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Asymptotic distributions of some test criteria for the mean vector with fewer observations than the dimension

Author

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  • Katayama, Shota
  • Kano, Yutaka
  • Srivastava, Muni S.

Abstract

The problem of hypothesis testing concerning the mean vector for high dimensional data has been investigated by many authors. They have proposed several test criteria and obtained their asymptotic distributions, under somewhat restrictive conditions, when both the sample size and the dimension tend to infinity. Indeed, the conditions used by these authors exclude a typical situation where the population covariance matrix has spiked eigenvalues, as for instance, the population covariance matrix with the compound symmetry structure (the variances are the same; the covariances are the same). In this paper, we relax their conditions to include such important cases, obtaining rather non-standard asymptotic distributions which are the convolution of normal and chi-squared distributions for the population covariance matrix with moderate spiked eigenvalues, and obtaining the asymptotic distributions in the form of convolutions of chi-square distributions for the population covariance matrix with quite spiked eigenvalues.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:jmvana:v:116:y:2013:i:c:p:410-421
    DOI: 10.1016/j.jmva.2013.01.008
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    References listed on IDEAS

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    1. Antonia Castaño-Martínez & Fernando López-Blázquez, 2005. "Distribution of a sum of weighted noncentral chi-square variables," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 14(2), pages 397-415, December.
    2. Coelho, Carlos A. & Marques, Filipe J., 2010. "Near-exact distributions for the independence and sphericity likelihood ratio test statistics," Journal of Multivariate Analysis, Elsevier, vol. 101(3), pages 583-593, March.
    3. Franklin Satterthwaite, 1941. "Synthesis of variance," Psychometrika, Springer;The Psychometric Society, vol. 6(5), pages 309-316, October.
    4. 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.
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    Cited by:

    1. Zhao, Junguang & Xu, Xingzhong, 2016. "A generalized likelihood ratio test for normal mean when p is greater than n," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 91-104.
    2. Jin-Ting Zhang & Bu Zhou & Jia Guo, 2022. "Testing high-dimensional mean vector with applications," Statistical Papers, Springer, vol. 63(4), pages 1105-1137, August.
    3. Pini, Alessia & Stamm, Aymeric & Vantini, Simone, 2018. "Hotelling’s T2 in separable Hilbert spaces," Journal of Multivariate Analysis, Elsevier, vol. 167(C), pages 284-305.

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