CALCULATION OF PREDICTION ERROR VARIANCES USING SPARSE-MATRIX METHODS

The use of exact and approximate algorithms to calculate prediction er ror variances using sparse matrix methods are demonstrated for an indi vidual animal effect including maternal effects. One exact algorithm i s substantially faster than two others. An approximation of the best e xact 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.