Selection of regression variables and contributions to the estimation of variance components

Abstract

This dissertation consists of two parts. In Chapter I an efficient procedure is described for identifying best regression subsets. In Chapter II the likelihood functions for the random, nested and random, classification analysis of variance models are analyzed. An iterative procedure for obtaining maximum likelihood estimates of variance components is described