Riccardo De Bin

• 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
• 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

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• 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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