Riccardo De Bin
Associate Professor
-
Statistics and Data Science
Norwegian version of this page
Email
debin@math.uio.no
Phone
+47 22855859
Mobile phone
+47-22855859
Room
815
Username
Visiting address
Moltke Moes vei 35
Niels Henrik Abels hus
0851 OSLO
Postal address
Postboks 1053 Blindern
0316 OSLO
SHORT CV
RESEARCH INTERESTS
- Asymptotic theory
- Boosting
- High-dimensional data
- Pseudo-likelihoods
- Resampling techniques
- Variable selection
TEACHING
- STK2100 - Maskinlæring og statistiske metoder for prediksjon og klassifikasjon
- STK2130 - Modelling by Stochastic Processes
- STK4030 - Statistical Learning: Advanced Regression and Classification
- STK-IN4300 - Statistical Learning Methods in Data Science
- STK9200 - Advanced Statistical Methods
- slides and code Applied Statistics 2019
Publications
- Fuchs, Mathias; Hornung, Roman; Boulesteix, Anne-Laure & De Bin, Riccardo (2020). On the asymptotic behaviour of the variance estimator of a U-statistic. Journal of Statistical Planning and Inference. ISSN 0378-3758. . doi: https://doi.org/10.1016/j.jspi.2020.03.003
- Belhechmi, Shaima; De Bin, Riccardo; Michiels, Stefan & Rotolo, Federico (2019). Prise en compte des groupes de biomarqueurs ou des voies biologiques dans les modèles de Cox pénalisés de haute dimension. Revue d'épidémiologie et de santé publique. ISSN 0398-7620. 67 Full text in Research Archive.
- De Bin, Riccardo; Boulesteix, Anne-Laure; Benner, Alex; Becker, Natalia & Sauerbrei, Willi (2019). Combining clinical and molecular data in regression prediction models: insights from a simulation study. Briefings in Bioinformatics. ISSN 1467-5463. . doi: 10.1093/bib/bbz136
- Volkmann, Alexander; De Bin, Riccardo; Sauerbrei, Willi & Boulesteix, Anne-Laure (2019). A plea for taking all available clinical information into account when assessing the predictive value of omics data. BMC Medical Research Methodology. ISSN 1471-2288. 19, s 1- 15 . doi: 10.1186/s12874-019-0802-0
- De Bin, Riccardo & Sauerbrei, Willi (2018). Handling co-dependence issues in resampling-based variable selection procedures: a simulation study. Journal of Statistical Computation and Simulation. ISSN 0094-9655. 88(1), s 28- 55 . doi: 10.1080/00949655.2017.1378654 Full text in Research Archive.
- van Gruting, Isabelle; Kluivers, Kirsten B; Sultan, Abdul H.; De Bin, Riccardo; Stankiewicz, Aleksandra; Blake, H. & Thakar, Ranee (2018). Has 4D transperineal ultrasound additional value over 2D transperineal ultrasound for diagnosing obstructed defaecation syndrome?. Ultrasound in Obstetrics and Gynecology. ISSN 0960-7692.
- Boulesteix, Anne-Laure; De Bin, Riccardo; Jiang, Xiaoyu & Fuchs, Mathias (2017). IPF-LASSO: Integrative L1-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics Data. Computational & Mathematical Methods in Medicine. ISSN 1748-670X. 2017 . doi: 10.1155/2017/7691937 Full text in Research Archive.
- De Bin, Riccardo (2017). Overview of Topics Related to Model Selection for Regression. Trends in Mathematics. ISSN 2297-0215. 7, s 77- 82
- De Bin, Riccardo; Boulesteix, Anne-Laure & Sauerbrei, Willi (2017). Detection of influential points as a byproduct of resampling-based variable selection procedures. Computational Statistics & Data Analysis. ISSN 0167-9473. 116, s 19- 31 . doi: 10.1016/j.csda.2017.07.001 Full text in Research Archive.
- Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure & De Bin, Riccardo (2017). On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Computational statistics (Zeitschrift). ISSN 0943-4062. s 1195- 1215 . doi: 10.1007/s00180-017-0773-8 Full text in Research Archive.
- van Gruting, Isabelle; Stankiewicz, Aleksandra; Kluivers, Kirsten B; De Bin, Riccardo; Blake, Helena; Sultan, Abdul H. & Thakar, Ranee (2017). Accuracy of Four Imaging Techniques for Diagnosis of Posterior Pelvic Floor Disorders. Obstetrics and Gynecology. ISSN 0029-7844. 130(5), s 1017- 1024 . doi: 10.1097/AOG.0000000000002245
- De Bin, Riccardo (2016). A note on the equivalence between conditional and random-effects likelihoods in exponential families. Statistics and Probability Letters. ISSN 0167-7152. 110, s 34- 38 . doi: 10.1016/j.spl.2015.12.002
- De Bin, Riccardo (2016). Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost. Computational statistics (Zeitschrift). ISSN 0943-4062. 31, s 513- 531
- De Bin, Riccardo; Janitza, Silke; Sauerbrei, Willi & Boulesteix, Anne-Laure (2016). Subsampling versus bootstrap in resampling-based model selection for multivariable regression. Biometrics. ISSN 0006-341X. 72(1), s 272- 280 . doi: 10.1111/biom.12381
- De Bin, Riccardo; Severini, Thomas A. & Sartori, Nicola (2015). Integrated likelihoods in models with stratum nuisance parameters. Electronic Journal of Statistics. ISSN 1935-7524. 7, s 1474- 1491
- De Bin, Riccardo; Herold, Tobias & Boulesteix, Anne-Laure (2014). Added predictive value of omics data: specific issues related to validation illustrated by two case studies. BMC Medical Research Methodology. ISSN 1471-2288. 14, s 117
- De Bin, Riccardo; Sauerbrei, Willi & Boulesteix, Anne-Laure (2014). Investigating the prediction ability of survival models based on both clinical and omics data: two case studies. Statistics in Medicine. ISSN 0277-6715. 33, s 5310- 5329
- De Bin, Riccardo & Risso, Davide (2011). A novel approach to the clustering of microarray data via nonparametric density estimation.. BMC Bioinformatics. ISSN 1471-2105. 12, s 49
- De Bin, Riccardo (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures.
- De Bin, Riccardo (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures.
- De Bin, Riccardo (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures.
- De Bin, Riccardo (2019). First-hitting-time models for high-dimensional data: A statistical boosting approach.
- De Bin, Riccardo (2019). Influence of single observations on the choice of the penalty parameter in ridge regression.
- De Bin, Riccardo (2019). Research group “Statistics and Data Science” and my research interests.
- De Bin, Riccardo (2019). Statistics meets machine learning: the example of statistical boosting.
- De Bin, Riccardo (2019). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- De Bin, Riccardo (2019). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- De Bin, Riccardo (2018). Combining low-dimensional clinical and high-dimensional molecular data in a survival prediction model.
- De Bin, Riccardo (2018). Integrated likelihoods in the context of distributed computing.
- De Bin, Riccardo (2018). Meta-analysis and selection for significance.
- De Bin, Riccardo (2018). On combining clinical and omics data in regression prediction models.
- De Bin, Riccardo (2018). On the equivalence between conditional and random-effects likelihoods in exponential families.
- De Bin, Riccardo (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data.
- De Bin, Riccardo (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data.
- De Bin, Riccardo (2018). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data.
- De Bin, Riccardo (2018). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- Fuchs, Mathias; Hornung, Roman; Boulesteix, Anne-Laure & De Bin, Riccardo (2018). An asymptotic test for the equality of error rates based on variance estimation of complete subsampling.
- De Bin, Riccardo (2017). Detection of influential points as a byproduct of resampling-based variable selection procedures.
- De Bin, Riccardo (2017). Extensions of threshold-hitting models to higher dimension.
- De Bin, Riccardo (2017). Integrated likelihoods in the presence of many nuisance parameters.
- De Bin, Riccardo (2017). Integrated likelihoods in the presence of many nuisance parameters.
- De Bin, Riccardo (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- De Bin, Riccardo (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- De Bin, Riccardo (2017). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
- De Bin, Riccardo (2016). Referee Report For: Three general concepts to improve risk prediction: good data, wisdom of the crowd, recalibration [version 1; referees: 2 approved with reservations]. F1000 Research. ISSN 2046-1402. 5, s 2671 . doi: 10.5256/f1000research.9340.r18283
Published Oct. 17, 2016 3:33 PM
- Last modified Sep. 22, 2019 5:07 PM