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2020 – today
- 2024
[j9]Mathias von Ottenbreit
, Riccardo De Bin:
Automatic piecewise linear regression. Comput. Stat. 39(4): 1867-1907 (2024)- [i3]Timo Braun, Anders Kvellestad, Riccardo De Bin:
GPTreeO: An R package for continual regression with dividing local Gaussian processes. CoRR abs/2410.01024 (2024) - 2023
- [j8]Shaima Belhechmi, Gwénaël Le Teuff, Riccardo De Bin, Federico Rotolo, Stefan Michiels:
Favoring the hierarchical constraint in penalized survival models for randomized trials in precision medicine. BMC Bioinform. 24(1): 96 (2023) - 2021
- [i2]Clara B. Salucci, Azzeddine Bakdi, Ingrid Kristine Glad, Erik Vanem, Riccardo De Bin:
Simple statistical models and sequential deep learning for Lithium-ion batteries degradation under dynamic conditions: Fractional Polynomials vs Neural Networks. CoRR abs/2102.08111 (2021) - [i1]Alise Danielle Midtfjord, Riccardo De Bin, Arne Bang Huseby:
A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data. CoRR abs/2107.04010 (2021) - 2020
- [j7]Riccardo De Bin, Anne-Laure Boulesteix, Axel Benner, Natalia Becker, Willi Sauerbrei:
Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings Bioinform. 21(6): 1904-1919 (2020) - [j6]Shaima Belhechmi, Riccardo De Bin, Federico Rotolo, Stefan Michiels:
Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models. BMC Bioinform. 21(1): 277 (2020)
2010 – 2019
- 2018
- [j5]Heidi Seibold, Christoph Bernau, Anne-Laure Boulesteix, Riccardo De Bin:
On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Comput. Stat. 33(3): 1195-1215 (2018) - 2017
- [j4]Anne-Laure Boulesteix, Riccardo De Bin, Xiaoyu Jiang, Mathias Fuchs:
IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Comput. Math. Methods Medicine 2017: 7691937:1-7691937:14 (2017) - [j3]Riccardo De Bin, Anne-Laure Boulesteix, Willi Sauerbrei:
Detection of influential points as a byproduct of resampling-based variable selection procedures. Comput. Stat. Data Anal. 116: 19-31 (2017) - 2016
- [j2]Riccardo De Bin:
Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost. Comput. Stat. 31(2): 513-531 (2016) - 2011
- [j1]Riccardo De Bin, Davide Risso:
A novel approach to the clustering of microarray data via nonparametric density estimation. BMC Bioinform. 12: 49 (2011)
Coauthor Index
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