Statistical Methods A NOTE ON BIAS IN REDUCED RANK ESTIMATES OF COVARIANCE MATRICES

Abstract

Fitting only the leading principal components allows genetic covariance matrices to be modelled parsimoniously, yielding reduced rank estimates. If principal components with non-zero variances are omitted from the model, genetic variation is moved into the covariance matrices for residuals or other random effects. The resulting bias in estimates of genetic eigen-values and -vectors is examined.

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Cite this paper

@inproceedings{Meyer2007StatisticalMA, title={Statistical Methods A NOTE ON BIAS IN REDUCED RANK ESTIMATES OF COVARIANCE MATRICES}, author={Karin Meyer and Mark A. Kirkpatrick}, year={2007} }