Statistical Methods A NOTE ON BIAS IN REDUCED RANK ESTIMATES OF COVARIANCE MATRICES
نویسندگان
چکیده
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.
منابع مشابه
Perils of parsimony: properties of reduced-rank estimates of genetic covariance matrices.
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