Riccardo De Bin
Riccardo De Bin
Associate Professor, Department of Mathematics, University of Oslo
Verified email at - Homepage
Cited by
Cited by
Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex
P Friederich, G dos Passos Gomes, R De Bin, A Aspuru-Guzik, D Balcells
Chemical Science 11 (18), 4584-4601, 2020
Subsampling versus bootstrapping in resampling-based model selection for multivariable regression
R De Bin, S Janitza, W Sauerbrei, AL Boulesteix
Biometrics 72 (1), 272-280, 2016
IPF-LASSO: integrative L1-penalized regression with penalty factors for prediction based on multi-omics data
AL Boulesteix, R De Bin, X Jiang, M Fuchs
Computational & Mathematical Methods in Medicine 2017, 2017
Investigating the prediction ability of survival models based on both clinical and omics data: two case studies
R De Bin, W Sauerbrei, AL Boulesteix
Statistics in Medicine 33 (30), 5310 - 5329, 2014
Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost
R De Bin
Computational Statistics 31, 513-531, 2016
Accuracy of four imaging techniques for diagnosis of posterior pelvic floor disorders
IMA van Gruting, A Stankiewicz, K Kluivers, R De Bin, H Blake, AH Sultan, ...
Obstetrics & Gynecology 130 (5), 1017-1024, 2017
A novel approach to the clustering of microarray data via nonparametric density estimation
R De Bin, D Risso
BMC bioinformatics 12, 1-8, 2011
On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models
RDB H Seibold, C Bernau, AL Boulesteix
Computational Statistics 33 (3), 1195–1215, 2018
Predicting time to graduation at a large enrollment American university
JM Aiken, R De Bin, M Hjorth-Jensen, MD Caballero
Plos one 15 (11), e0242334, 2020
Multivariable fractional polynomials for lithium-ion batteries degradation models under dynamic conditions
CB Salucci, A Bakdi, IK Glad, E Vanem, R De Bin
Journal of Energy Storage 52, 104903, 2022
Integrated likelihoods in models with stratum nuisance parameters
R De Bin, N Sartori, TA Severini
Electronic Journal of Statistics 9, 1474-1491, 2015
Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resampling
C Wallisch, D Dunkler, G Rauch, R De Bin, G Heinze
Statistics in Medicine 40 (2), 369-381, 2021
A decision support system for safer airplane landings: Predicting runway conditions using XGBoost and explainable AI
AD Midtfjord, R De Bin, AB Huseby
Cold Regions Science and Technology 199, 103556, 2022
A novel semi-supervised learning approach for State of Health monitoring of maritime lithium-ion batteries
CB Salucci, A Bakdi, IK Glad, E Vanem, R De Bin
Journal of Power Sources 556, 232429, 2023
On the asymptotic behaviour of the variance estimator of a U-statistic
M Fuchs, R Hornung, AL Boulesteix, R De Bin
Journal of Statistical Planning and Inference 209, 101-111, 2020
A plea for taking all available clinical information into account when assessing the predictive value of omics data
A Volkmann, R De Bin, W Sauerbrei, AL Boulesteix
BMC medical research methodology 19, 1-15, 2019
Framework for evaluating statistical models in physics education research
JM Aiken, R De Bin, HJ Lewandowski, MD Caballero
Physical Review Physics Education Research 17 (2), 020104, 2021
Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models
S Belhechmi, RD Bin, F Rotolo, S Michiels
BMC bioinformatics 21, 1-20, 2020
Deep learning metal complex properties with natural quantum graphs
H Kneiding, R Lukin, L Lang, S Reine, TB Pedersen, R De Bin, D Balcells
Digital Discovery 2 (3), 618-633, 2023
Added predictive value of omics data: specific issues related to validation illustrated by two case studies
R De Bin, T Herold, AL Boulesteix
BMC Medical Research Methodology 14, 117, 2014
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