Riccardo De Bin

Førsteamanuensis - Statistikk og biostatistikk
Bilde av Riccardo De Bin
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Mobiltelefon +47-22855859
Rom 827
Besøksadresse Ullevål stadion Sognsveien 77 B 0855 OSLO
Postadresse Postboks 1053 Blindern 0316 OSLO
Andre tilknytninger Den internasjonale sommerskole


2016-now Associate Professor at University of Oslo (Norway)
2016-2016 Assistant Professor at Radboud University Medical Centre of Nijmegen (Netherlands)
2012-2016 Postdoctoral Fellow at University of Munich (Germany)
2012-2012 Research Assistant at University of Padova (Italy)
2009-2012 Ph.D. in Statistics at University of Padova (Italy)
2010-2011 Predoctoral Fellow at Northwestern University (Evanston (IL) - USA)



  • Asymptotic theory
  • Boosting
  • High-dimensional data
  • Pseudo-likelihoods
  • Resampling techniques
  • Variable selection



Emneord: Statistikk og biostatistikk


  • 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 Fulltekst i vitenarkiv.
  • Seibold, Heidi; Bernau, Christoph; Boulesteix, Anne-Laure & De Bin, Riccardo (2018). On the choice and influence of the number of boosting steps for high-dimensional linear Cox-models. Computational statistics (Zeitschrift).  ISSN 0943-4062. . doi: 10.1007/s00180-017-0773-8 Fulltekst i vitenarkiv.
  • 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 Fulltekst i vitenarkiv.
  • 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 Fulltekst i vitenarkiv.
  • 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

Se alle arbeider i Cristin

  • 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

Se alle arbeider i Cristin

Publisert 17. okt. 2016 15:33 - Sist endret 18. okt. 2017 15:24