| Author(s) |
Meyer, Karin
|
| Publication Date |
2015
|
| Abstract |
A scheme for penalized estimation of genetic covariance matrices free from tuning - using default settings for the strength or penalization - is described and its efficacy is demonstrated by simulation. Estimates of genetic covariance matrices, ΣG, are known to be afflicted by substantial sampling errors, increasing markedly with the number of traits considered. 'Regularization', i.e. modification of estimators to reduce sampling variation at the expense of a small, additional bias, has been advocated to obtain estimates closer to the population values. An early suggestion by Hayes and Hill (1981, 'bending') has been to shrink the canonical eigenvalues... towards their mean.
|
| Citation |
Proceedings of the Association for the Advancement of Animal Breeding and Genetics, v.21, p. 278-281
|
| ISBN |
9780646945545
|
| ISSN |
1328-3227
|
| Link | |
| Language |
en
|
| Publisher |
Association for the Advancement of Animal Breeding and Genetics (AAABG)
|
| Title |
Mildly Penalized Maximum Likelihood Estimation of Genetic Covariances Matrices Without Tuning
|
| Type of document |
Conference Publication
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| Entity Type |
Publication
|
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