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)

@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}
}