"Tests for Multivariate Analysis of Variance in High Dimension Under Non-Normality"
In this article, we consider the problem of testing the equality of mean vectors of dimension ρ of several groups with a common unknown non-singular covariance matrix Σ, based on <em>N</em> independent observation vectors where <em>N</em> may be less than the dimension ρ. This problem, known in the literature as the Multivariate Analysis of variance (MANOVA) in high-dimension has recently been considered in the statistical literature by Srivastava and Fujikoshi[7], Srivastava [5] and Schott[3]. All these tests are not invariant under the change of units of measurements. On the lines of Srivastava and Du[8] and Srivastava[6], we propose a test that has the above invariance property. The null and the non-null distributions are derived under the assumption that (<em>N</em>, ρ) → ∞ and <em>N</em> may be less than ρ and the observation vectors follow a general non-normal model.
Year of publication: |
2011-12
|
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Authors: | Srivastava, Muni S. ; Kubokawa, Tatsuya |
Institutions: | Center for International Research on the Japanese Economy (CIRJE), Faculty of Economics |
Saved in:
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