A test for the mean vector with fewer observations than the dimension

Muni S. Srivastava and Meng Du

Journal of Multivariate Analysis, 2008, vol. 99, issue 3, 386-402

Abstract: In 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.

Keywords: Asymptotic; distribution; DNA; microarray; Multivariate; normal; Power; comparison; Significance; test (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (56)

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