TESTING HYPOTHESES FOR VARIANCE-COMPONENTS IN MIXED LINEAR-MODELS

In the paper the problem of testing hypotheses for variance components in mixed linear models is considered. It is assumed that covariance m atrices commute after using the usual invariance procedure with respec t to the group of translations. The test for vanishing of single varia nce component is based on the locally best quadratic unbiased estimato r of this component and rejects hypothesis if the ratio of positive an d negative part of this estimator is sufficiently large. The power of this test with powers of other four tests for two-way classification m odels corresponding to block design is compared.