29 citations · 82 across the 9 of their papers we have counts for
7 papers · 1 filter
Fridge: focused fine-tuning of ridge regression for personalize predictions
Kristoffer Herland Hellton, Nils Lid Hjort
Statistical prediction methods typically require some form of fine-tuning of tuning parameter(s), with -fold cross-validation as the canonical procedure. For ridge regression th…
Penalized angular regression for personalized predictions
Kristoffer H. Hellton
Personalization is becoming an important feature in many predictive applications. We introduce a penalized regression method implementing personalization inherently in the penalty.…
Influence of single observations on the choice of the penalty parameter in ridge regression
Kristoffer H. Hellton, Camilla Lingjærde, Riccardo De Bin
Penalized regression methods such as ridge regression heavily rely on the choice of a tuning or penalty parameter, which is often computed via cross-validation. Discrepancies in th…
Multivariate postprocessing methods for high-dimensional seasonal weather forecasts
Claudio Heinrich, Kristoffer H. Hellton, Alex Lenkoski +1
Seasonal weather forecasts are crucial for long-term planning in many practical situations and skillful forecasts may have substantial economic and humanitarian implications. Curre…
Multiple Model-Free Knockoffs
Lars Holden, Kristoffer Hellton
Model-free knockoffs is a recently proposed technique for identifying covariates that is likely to have an effect on a response variable. The method is an efficient method to contr…
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…
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…