Testing the structure of the covariance matrix with fewer observations than the dimension

Abstract

We consider two hypothesis testing problems with N independent observations on a single m-vector, when m > N , and the N observations on the random m-vector are independently and identically distributed as multivariate normal with mean vector μ and covariance matrix Σ, both unknown. In the first problem, the m-vector is partitioned into two subvectors of… (More)
DOI: 10.1016/j.jmva.2012.06.004

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@article{Srivastava2012TestingTS, title={Testing the structure of the covariance matrix with fewer observations than the dimension}, author={Muni S. Srivastava and N. Reid}, journal={J. Multivariate Analysis}, year={2012}, volume={112}, pages={156-171} }