Estimating Variance Components in a Class of Mixed Models by Restricted Maximum Likelihood

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Abstract
An efficient algorithm is described for computing restricted maximum likelihood estimates of variance components in a class of models. The class of models is characterized by effects to be absorbed, which are nested within herds, other fixed effects, random sire effects, one other group of random effects nested within herds, and a random residual. The model can be generalized to account for relationships among sires. Matrix inversion is not used. A realistic example is presented.