Post-Estimation Penalization: More 'PEP' for Estimates of Genetic Covariance Matrices

Abstract

Maximum likelihood estimation of genetic covariances subject to a penalty to reduce sampling variation has been shown to yield improved estimates, especially for analyses comprising many traits. However, this can increase computational requirements substantially. Similarly, penalties have been found to be beneficial in a maximum likelihood based approach for pooling results from analyses of subsets of traits. This paper examines the scope for using the latter method to apply penalties to results from multivariate analyses in a computationally undemanding post-estimation step. A simulation study is presented demonstrating that even slight changes to estimates in this way can result in 'regularized' values markedly closer to population values than standard, unpenalized estimates.