Tests for Multivariate Analysis of Variance in High Dimension Under Non-Normality
AbstractIn 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 N independent observation vectors where N 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, Srivastava  and Schott. All these tests are not invariant under the change of units of measurements. On the lines of Srivastava and Du and Srivastava, we propose a test that has the above invariance property. The null and the non-null distributions are derived under the assumption that ( N, ρ) → ∞ and N may be less than ρ and the observation vectors follow a general non-normal model.
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Bibliographic InfoPaper provided by CIRJE, Faculty of Economics, University of Tokyo in its series CIRJE F-Series with number CIRJE-F-831.
Length: 21 pages
Date of creation: Dec 2011
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