1995 年 16 巻 1.2 号 p. 1-8
Assuming a specific type of data in the field of animal breeding, the iteration equations based on the expectation-maximization algorithm are derived for the restricted maximum likelihood estimation of variance components in a Sire and Dam Model. The application of the iteration equations to the data leads to the same estimates of additive genetic and environmental variances as those under the lndividual Animal Model. With the procedure using the iteration equations, compared to the case for the lndividual Animal Model, the size of the coefficient matrix to be inverted of the mixed model equations relatively becomes small, and the speed of convergence of the estimates becomes rather fast. Consequently, the total computational burden to obtain the proper estimates of additive genetic and environmental variances is expected to be considerably reduced in the proposed procedure. A numerical illustration, comparing the proposed procedure with the lndividual Animal Model procedure, is given using simulated carcass data on beef cattle.