A test for the mean vector with fewer observations than the dimension
AbstractIn this paper, we consider a test for the mean vector of independent and identically distributed multivariate normal random vectors where the dimension p is larger than or equal to the number of observations N. This test is invariant under scalar transformations of each component of the random vector. Theories and simulation results show that the proposed test is superior to other two tests available in the literature. Interest in such significance test for high-dimensional data is motivated by DNA microarrays. However, the methodology is valid for any application which involves high-dimensional data.
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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 99 (2008)
Issue (Month): 3 (March)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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.
- 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.
- 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.
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