29 citations · 82 across the 9 of their papers we have counts for
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stat.ME2014
Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study
Kristoffer Hellton, Magne Thoresen
When measuring a range of different genomic, epigenomic, transcriptomic and other variables, an integrative approach to analysis can strengthen inference and give new insights. Thi…
stat.ME2014★ 29 cited
Covariate Selection in High-Dimensional Generalized Linear Models With Measurement Error
Øystein Sørensen, Arnoldo Frigessi, Magne Thoresen
In many problems involving generalized linear models, the covariates are subject to measurement error. When the number of covariates p exceeds the sample size n, regularized method…
math.ST2014★ 12 cited
When and why are principal component scores a good tool for visualizing high-dimensional data?
Kristoffer Hellton, Magne Thoresen
Principal component analysis (PCA) is a popular dimension reduction technique often used to visualize high-dimensional data structures. In genomics, this can involve millions of va…