| Author(s) |
Meyer, Karin
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| Publication Date |
2014
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| Abstract |
Penalized REML estimation can substantially reduce sampling variation in estimates of covariance matrices, and yield estimates of genetic parameters closer to population values than standard analyses. A number of suitable penalties based on prior distributions of correlation matrices from the Bayesian literature are described, and a simulation study is presented demonstrating their efficacy. Results show that reductions of 'loss' in estimates of the genetic covariance matrix, a conglomerate of sampling variance and bias, well over 50% are readily obtained for multivariate analyses of small samples. Default settings for a mild degree of penalization are proposed, which make such analyses suitable for routine use without increasing computational requirements.
|
| Citation |
Proceedings of the 10th World Congress on Genetics Applied to Livestock Production (WCGALP) (Methods and Tools: Statistical methods - linear and nonlinear models), p. 1-3
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| Link | |
| Publisher |
American Society of Animal Science
|
| Title |
Improving REML estimates of genetic parameters through penalties on correlation matrices
|
| Type of document |
Conference Publication
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| Entity Type |
Publication
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