Riccardo De Bin
Riccardo De Bin
Associate Professor, Department of Mathematics, University of Oslo
Verified email at - Homepage
Cited by
Cited by
IPF-LASSO: integrative-penalized regression with penalty factors for prediction based on multi-omics data
AL Boulesteix, R De Bin, X Jiang, M Fuchs
Computational and mathematical methods in medicine 2017, 2017
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
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
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 (2), 513-531, 2016
A novel approach to the clustering of microarray data via nonparametric density estimation
R De Bin, D Risso
BMC bioinformatics 12 (1), 1-8, 2011
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
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
Integrated likelihoods in models with stratum nuisance parameters
R De Bin, N Sartori, TA Severini
Electronic Journal of Statistics 9, 1474-1491, 2015
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
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
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
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
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), 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
Does 4D transperineal ultrasound have additional value over 2D transperineal ultrasound for diagnosing posterior pelvic floor disorders in women with obstructed defecation …
IMA van Gruting, K Kluivers, AH Sultan, R De Bin, A Stankiewicz, H Blake, ...
Ultrasound in Obstetrics & Gynecology 52 (6), 784-791, 2018
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), 1-20, 2020
Combining clinical and molecular data in regression prediction models: insights from a simulation study
R De Bin, AL Boulesteix, A Benner, N Becker, W Sauerbrei
Briefings in Bioinformatics 21 (6), 1904-1919, 2020
Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions
C Bertinelli Salucci, A Bakdi, IK Glad, E Vanem, R De Bin
Journal of Energy Storage 52 (Part B), 2022
Modelling publication bias and p‐hacking
J Moss, R De Bin
Biometrics, 2021
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