Thursday, 13:45 - 14:10 h, Room: MA 649


Mihaela Pricop-Jeckstadt
Genomic selection and iterative regularization methods

Coauthor: Norbert Reinsch


In genomic selection it is expected that genetic information contributes to selection for difficult traits like traits with low heritability, traits which are hard to measure or sex limited traits.
The availability of dense markers covering the whole genome leads to genomic methods aiming for
estimating the effect of each of the available singlenucleotide polymorphism. Hence, we propose a semiparametric method and an iterative regularization approach for high-dimensional but small sample-sized data. Numerical challenges like model selection, the estimation of the predictive ability and the choice of the regularization parameter are discussed and illustrated by simulated and real data examples.


Talk 2 of the invited session Thu.2.MA 649
"Variational methods in inverse problems" [...]
Cluster 24
"Variational analysis" [...]


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