Multivariate analysis of variance with fewer observations than the dimension

Muni S. Srivastava and Yasunori Fujikoshi

Journal of Multivariate Analysis, 2006, vol. 97, issue 9, 1927-1940

Abstract: In this article, we consider the problem of testing a linear hypothesis in a multivariate linear regression model which includes the case of testing the equality of mean vectors of several multivariate normal populations with common covariance matrix [Sigma], the so-called multivariate analysis of variance or MANOVA problem. However, we have fewer observations than the dimension of the random vectors. Two tests are proposed and their asymptotic distributions under the hypothesis as well as under the alternatives are given under some mild conditions. A theoretical comparison of these powers is made.

Keywords: Distribution; of; test; statistics; DNA; microarray; data; Fewer; observations; than; dimension; Moore-Penrose; inverse; Multivariate; analysis; of; variance; Singular; Wishart (search for similar items in EconPapers)
Date: 2006
References: View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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