Two step-down tests for equality of covariance matrices

Abstract

The classical problem of testing the equality of the covariance matrices from k ≥ 2 p-dimensional normal populations is reexamined. The likelihood ratio (LR) statistic, also called Bartlett's statistic, can be decomposed in two ways, corresponding to two distinct component-wise decompositions of the null hypothesis in terms of the covariance matrices or precision matrices, respectively. The factors of the LR statistic that appear in these two decompositions can be interpreted as conditional and unconditional LR statistics for the component-wise null hypotheses, and their mutual independence under the null hypothesis allows the determination of the overall significance level. © 2006 Elsevier Inc. All rights reserved.