Inferring fixed effects in a mixed linear model from an integrated likelihood
Abstract:A new method for likelihood-based inference of fixed effects in mixed linear models, with variance components treated as nuisance parameters, is presented. The method uses uniform-integration of the likelihood; the implementation employs the expectation-maximization (EM) algorithm for elimination of all nuisances, viewing random effects and variance components as missing data. In a simulation of a grazing trial, the procedure was compared with four widely used estimators of fixed effects in mixed models, and found to be competitive. An analysis of body weight in freshwater crayfish was conducted to illustrate the feasibility of the methodology in a real situation. The method is a useful non-Bayesian alternative to maximum likelihood and estimated generalized least-squares, as it accounts for nuisance variances.
Document Type: Research Article
Affiliations: 1: Department of Animal Sciences, University of Wisconsin, Madison, USA 2: Department of Genetics and Biotechnology, Faculty of Agricultural Sciences, University of Aarhus, Tjele, Denmark
Publication date: 2007-12-01