Studies of a Class of Covariance Structure Models

Abstract

Abstract This article demonstrates the usefulness of analysis of covariance structure procedures for estimating variance components under more general assumptions than are provided in the typical mixed-model analysis of variance. A sequence of eight models is presented based on varying degrees of restrictive assumptions. Maximum likelihood procedures are employed in the estimation of the parameters of the models. The procedures are applied to simulated data and to an empirical example which shows the necessity of the more general assumptions.