2022-01-19T13:37:25Zhttps://repository.dl.itc.u-tokyo.ac.jp/oaioai:repository.dl.itc.u-tokyo.ac.jp:000429072021-03-02T02:05:40ZTests for Multivariate Analysis of Variance in High Dimension Under Non-NormalitySrivastava, Muni S.98666Kubokawa, Tatsuya98667335Asymptotic distributionshigh dimensionMANOVAmultivariate linear modelnon-normal modelsample size smaller than dimensionAMS 1991 subject classification: primary 62H15, Secondary 62F05application/pdfIn 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[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 (N, ρ) → ∞ and N may be less than ρ and the observation vectors follow a general non-normal model.Revised in January 2011.本文フィルはリンク先を参照のことtechnical report日本経済国際共同センター2011-12Discussion paper series. CIRJE-FCIRJE-F-831AA11450569enghttp://www.cirje.e.u-tokyo.ac.jp/research/dp/2011/2011cf831ab.htmlmetadata only access