Inferences about dependent normal populations are usually obtained considering asymptotic tests based on the maximization likelihood functions. However, if the number of populations and/or variables considered are too high one way have convergence problems with the numerical methods used to obtain the maximum likelihood estimators. This work aimed to illustrate, using a real data set, the application of a test to compare covariance matrices of correlated populations using a statistic based on generalized variances ratio, whose empirical distribution was obtained via bootstrap methods.
Bootstrap; covariances; generalized variances
