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A note on bias in reduced rank estimates of covariance matrices

  • Karin Meyer
  • , Mark Kirkpatrick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.
Original languageEnglish
Title of host publicationProceedings of the Seventeenth Conference for the Advancement of Animal Breeding and Genetics
EditorsAAABG: Association for the Advancement of Animal Breeding, Genetics
Place of PublicationArmidale, Australia
PublisherAssociation for the Advancement of Animal Breeding and Genetics (AAABG)
Pages154-157
Volume17
ISBN (Print)1921208139
Publication statusPublished - 2007
EventAAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics - University of New England, Armidale, Australia
Duration: 23 Sept 200726 Sept 2007

Conference

ConferenceAAABG 2007: 17th Conference of the Association for the Advancement of Animal Breeding and Genetics
CityArmidale, Australia
Period23/09/0726/09/07

Keywords

  • Quantitative Genetics (incl Disease and Trait Mapping Genetics)

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