ABSTRACT
We study a procedure in the framework of ranking and selection theory to identify multivariate normal observations that have different covariance structures from a control covariance matrix. Simulation results are presented to illustrate that, for a sample of data contaminated with non-homogeneous observations, our selection procedure improves the performance of the hypothesis testing for a signal in terms of the probability of type II error while the level of significance is held at a constant.
