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Kristoffer H. Hellton
Kristoffer H. Hellton
Oslo Metropolitan University
Verifisert e-postadresse på oslomet.no
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Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
MP Menden, D Wang, MJ Mason, B Szalai, KC Bulusu, Y Guan, T Yu, ...
Nature communications 10 (1), 2674, 2019
2702019
Covariate selection in high-dimensional generalized linear models with measurement error
Ø Sørensen, KH Hellton, A Frigessi, M Thoresen
Journal of Computational and Graphical Statistics 27 (4), 739-749, 2018
512018
Integrative clustering of high-dimensional data with joint and individual clusters
KH Hellton, M Thoresen
Biostatistics 17 (3), 537-548, 2016
362016
The impact of measurement error on principal component analysis
KH Hellton, M Thoresen
Scandinavian Journal of Statistics 41 (4), 1051-1063, 2014
322014
Fridge: Focused fine‐tuning of ridge regression for personalized predictions
KH Hellton, NL Hjort
Statistics in Medicine 37 (8), 1290-1303, 2018
272018
Bayesian regional flood frequency analysis for large catchments
TL Thorarinsdottir, KH Hellton, GH Steinbakk, L Schlichting, K Engeland
Water Resources Research 54 (9), 6929-6947, 2018
252018
Multivariate postprocessing methods for high-dimensional seasonal weather forecasts
C Heinrich, KH Hellton, A Lenkoski, TL Thorarinsdottir
Journal of the American Statistical Association 116 (535), 1048-1059, 2021
162021
Tuning parameter calibration for prediction in personalized medicine
ST Huang, Y Düren, KH Hellton, J Lederer
arXiv preprint arXiv:1909.10635, 2019
112019
When and Why are Principal Component Scores a Good Tool for Visualizing High‐dimensional Data?
KH Hellton, M Thoresen
Scandinavian Journal of Statistics 44 (3), 581–597, 2017
112017
The truth behind the zeros: a new approach to principal component analysis of the Neuropsychiatric Inventory
KH Hellton, J Cummings, AO Vik-Mo, JE Nordrehaug, D Aarsland, ...
Multivariate behavioral research 56 (1), 70-85, 2021
72021
Real-time prediction of propulsion motor overheating using machine learning
KH Hellton, M Tveten, M Stakkeland, S Engebretsen, O Haug, M Aldrin
Journal of Marine Engineering & Technology 21 (6), 334-342, 2022
62022
Asymptotic distribution of principal component scores for pervasive, high-dimensional eigenvectors
K Hellton, M Thoresen
arXiv preprint arXiv:1401.2781, 2014
52014
Tuning parameter calibration for personalized prediction in medicine
ST Huang, Y Düren, KH Hellton, J Lederer
Electronic Journal of Statistics 15 (2), 5310-5332, 2021
42021
Multiple model-free knockoffs
L Holden, K Hellton
arXiv preprint arXiv:1812.04928, 2018
42018
An agent-based model with social interactions for scalable probabilistic prediction of performance of a new product
R Parviero, KH Hellton, O Haug, K Engø-Monsen, H Rognebakke, ...
International Journal of Information Management Data Insights 2 (2), 100127, 2022
32022
Influence of single observations on the choice of the penalty parameter in ridge regression
KH Hellton, C Lingjærde, R De Bin
arXiv preprint arXiv:1911.03662, 2019
32019
Integrative clustering of high-dimensional data with joint and individual clusters, with an application to the Metabric study
K Hellton, M Thoresen
arXiv preprint arXiv:1410.8679, 2014
32014
Penalized angular regression for personalized predictions
KH Hellton
Scandinavian Journal of Statistics 50 (1), 184-212, 2023
12023
Verdens første p-verdi
KH Hellton, J Røislien
Tidsskrift for Den norske legeforening, 2017
12017
Asymptotic distribution of principal component scores connected to pervasive, high-dimensional eigenvectors
K Hellton, M Thoresen
12013
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Artikler 1–20