Calculation of prediction error variances using sparse matrix methods

The use of exact and approximate algorithms to calculate prediction error variances using sparse matrix methods are demonstrated for an individual animal effect including maternal effects. One exact algorithm is substantially faster than two others. An approximation of the best exact method gave an acceptable level of reliabilities and reduced the computation by a factor of approximately fifty compared with the exact computation and is routine in national beef evaluation in Britain.