Riccardo De Bin

Associate Professor - Statistics and Data Science
Image of Riccardo De Bin
Norwegian version of this page
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

 

Tags: Statistics, Statistics and biostatistics

Publications

  • Belhechmi, Shaima; De Bin, Riccardo; Rotolo, Federico & Michiels, Stefan (2020). Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression models. BMC Bioinformatics.  ISSN 1471-2105.  21
  • Friederich, Pascal; Dos Passos Gomes, Gabriel; De Bin, Riccardo; Aspuru-Guzik, Alán & Balcells, David (2020). Machine learning dihydrogen activation in the chemical space surrounding Vaska's complex. Chemical Science.  ISSN 2041-6520.  11(18), s 4584- 4601 . doi: 10.1039/d0sc00445f
  • 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 Full text in Research Archive.
  • 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 Full text in Research Archive.
  • 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). Does 4D transperineal ultrasound have additional value over 2D transperineal ultrasound for diagnosing posterior pelvic floor disorders in women with obstructed defaecation syndrome?. Ultrasound in Obstetrics and Gynecology.  ISSN 0960-7692.  52, s 784- 791
  • 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

View all works in Cristin

  • De Bin, Riccardo (2020). A brief overview on topics related to Statistical Learning.
  • De Bin, Riccardo (2020). Statistical Learning.
  • 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

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Published Oct. 17, 2016 3:33 PM - Last modified Sep. 22, 2019 5:07 PM

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