Skip to Main content Skip to Navigation
Journal articles

On the accuracy in high dimensional linear models and its application to genomic selection

Abstract : Genomic selection, a hot topic in genetics, consists in predicting breeding values of selection candidates, using a large number of genetic markers , due to the recent progress in molecular biology. One of the most popular method chosen by geneticists is Ridge regression. In this context, we focus on some predictive aspects of Ridge regression and present theoretical results regarding the accuracy criteria, i.e., the correlation between predicted value and true value. We show the influence of the singular values, the regularization parameter , and the projection of the signal on the space spanned by the rows of the design matrix. Asymptotic results, in a high dimensional framework, are also given, and we prove that the convergence to an optimal accuracy highly depends on a weighted projection of the signal on each subspace. We discuss also on how to improve the prediction. Last, illustrations on simulated and real data are proposed.
Document type :
Journal articles
Complete list of metadata

Cited literature [29 references]  Display  Hide  Download
Contributor : Charles-Elie Rabier <>
Submitted on : Sunday, March 11, 2018 - 11:20:04 AM
Last modification on : Wednesday, June 9, 2021 - 10:00:09 AM
Long-term archiving on: : Tuesday, June 12, 2018 - 12:16:30 PM


Files produced by the author(s)



Charles-Elie Rabier, Brigitte Mangin, Simona Grusea. On the accuracy in high dimensional linear models and its application to genomic selection. Scandinavian Journal of Statistics, Wiley, 2019, 46 (1), pp.289-313. ⟨10.1111/sjos.12352⟩. ⟨hal-01456310v2⟩



Record views


Files downloads