Abstract
In general, all selection processes first rank the candidates using some function of the observed data and then choose the selected portion as those with the largest (or smallest) values of that function. More explicitly, a function of y, an n x 1 vector of observed data records, is used to predict g, a q × 1 vector of genetic values of q genetic entities. The vector of predicted genetic values is calculated from ĝ = f(y) and the genotypes with the best ĝ values are selected.
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© 1989 Springer Science+Business Media Dordrecht
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White, T.L., Hodge, G.R. (1989). Theory of Best Linear Prediction. In: Predicting Breeding Values with Applications in Forest Tree Improvement. Forestry Sciences, vol 33. Springer, Dordrecht. https://doi.org/10.1007/978-94-015-7833-2_4
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DOI: https://doi.org/10.1007/978-94-015-7833-2_4
Publisher Name: Springer, Dordrecht
Print ISBN: 978-90-481-4055-8
Online ISBN: 978-94-015-7833-2
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