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An 'average information' restricted maximum likelihood algorithm for estimating reduced rank genetic covariance matrices or covariance functions for animal models with equal design matrices

1997
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Genetics Selection Evolution
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A Quasi-Newton Restricted Maximum Likelihood algorithm which approximates the Hessian matrix with the average of observed and expected information is described for the estimation of covariance components or covariance functions under a linear mixed model. The computing strategy outlined relies on sparse matrix tools and automatic differentiation of a matrix, and does not require inversion of large, sparse matrices. For the special case of a model with only one random factor and equal design

doi:10.1051/gse:19970201
fatcat:jbwgzfueendevingpovuyvgfne