Better estimates of genetic covariance matrices by "bending" using penalized maximum likelihood.

Abstract

Obtaining accurate estimates of the genetic covariance matrix Sigma(G) for multivariate data is a fundamental task in quantitative genetics and important for both evolutionary biologists and plant or animal breeders. Classical methods for estimating Sigma(G) are well known to suffer from substantial sampling errors; importantly, its leading eigenvalues are… (More)
DOI: 10.1534/genetics.109.113381

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