Skip to main content

Inferring fixed effects in a mixed linear model from an integrated likelihood

Buy Article:

$63.00 plus tax (Refund Policy)

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

DOI: https://doi.org/10.1080/09064700801959379

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

More about this publication?
  • Access Key
  • Free content
  • Partial Free content
  • New content
  • Open access content
  • Partial Open access content
  • Subscribed content
  • Partial Subscribed content
  • Free trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more