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A Monte-Carlo algorithm for maximum likelihood estimation of variance components
1996
Genetics Selection Evolution
A new algorithm for finding maximum likelihood (ML) solutions to variance components is introduced. This algorithm first treats random effects as fixed, then expresses the pseudo-fixed effects as linear transformations of a set of standard normal deviates which eventually are integrated out numerically through Monte-Carlo simulation. An iterative algorithm is employed to estimate the standard deviation (rather than the variance) of the random effects. This method is conceptually simple and easy
doi:10.1186/1297-9686-28-4-329
fatcat:eymh2bbpq5ajtn7ykv7ssfja2m