Riccardo De Bin

Førsteamanuensis - Statistikk og Data Science
Bilde av Riccardo De Bin
English version of this page
Telefon +47 22855859
Mobiltelefon +47-22855859
Rom 815
Brukernavn
Besøksadresse Moltke Moes vei 35 Niels Henrik Abels hus 0851 Oslo
Postadresse Postboks 1053 Blindern 0316 Oslo

SHORT CV

 

RESEARCH INTERESTS

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

 

TEACHING

Emneord: Statistikk og biostatistikk

Publikasjoner

  • Bertinelli Salucci, Clara; Bakdi, Azzeddine; Glad, Ingrid Kristine; Vanem, Erik & De Bin, Riccardo (2022). Multivariable Fractional Polynomials for lithium-ion batteries degradation models under dynamic conditions. Journal of Energy Storage. ISSN 2352-152X. 52. doi: 10.1016/j.est.2022.104903. Fulltekst i vitenarkiv
  • Midtfjord, Alise Danielle; De Bin, Riccardo & Huseby, Arne Bang (2022). A decision support system for safer airplane landings: Predicting runway conditions using XGBoost and explainable AI. Cold Regions Science and Technology. ISSN 0165-232X. 199. doi: 10.1016/j.coldregions.2022.103556. Fulltekst i vitenarkiv
  • De Bin, Riccardo & Stikbakke, Vegard (2022). A boosting first-hitting-time model for survival analysis in high-dimensional settings. Lifetime Data Analysis. ISSN 1380-7870. doi: 10.1007/s10985-022-09553-9. Fulltekst i vitenarkiv
  • Moss, Jonas & De Bin, Riccardo (2021). Modelling publication bias and p-hacking. Biometrics. ISSN 0006-341X. doi: 10.1111/biom.13560. Fulltekst i vitenarkiv
  • Midtfjord, Alise Danielle; De Bin, Riccardo & Huseby, Arne Bang (2021). A Machine Learning Approach to Safer Airplane Landings: Predicting Runway Conditions using Weather and Flight Data. arXiv.org. ISSN 2331-8422.
  • Aiken, John Mark; De Bin, Riccardo; Lewandowski, Heather & Caballero, Marcos Daniel (2021). Framework for evaluating statistical models in physics education research. Physical Review Physics Education Research. ISSN 2469-9896. 17(2). doi: 10.1103/PhysRevPhysEducRes.17.020104. Fulltekst i vitenarkiv
  • Aiken, John Mark; De Bin, Riccardo; Hjorth-Jensen, Morten & Caballero, Marcos Daniel (2020). Predicting time to graduation at a large enrollment American university. PLOS ONE. ISSN 1932-6203. doi: 10.1371/journal.pone.0242334. Fulltekst i vitenarkiv
  • Wallisch, Christine; Dunkler, Daniela; Rauch, Geraldine; De Bin, Riccardo & Heinze, Georg (2020). Selection of variables for multivariable models: opportunities and limitations in quantifying model stability by resampling. Statistics in Medicine. ISSN 0277-6715. 40(2), s. 369–381. doi: 10.1002/sim.8779. Fulltekst i vitenarkiv
  • 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. doi: 10.1186/s12859-020-03618-y. Fulltekst i vitenarkiv
  • 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. Fulltekst i vitenarkiv
  • 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. 209, s. 101–111. doi: 10.1016/j.jspi.2020.03.003.
  • 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. Fulltekst i vitenarkiv
  • 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. Fulltekst i vitenarkiv
  • 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. Fulltekst i vitenarkiv
  • van Gruting, Isabelle; Kluivers, Kirsten B; Sultan, Abdul H.; De Bin, Riccardo; Stankiewicz, Aleksandra & Blake, H. [Vis alle 7 forfattere av denne artikkelen] (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.
  • 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 (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. Fulltekst i vitenarkiv
  • van Gruting, Isabelle; Stankiewicz, Aleksandra; Kluivers, Kirsten B; De Bin, Riccardo; Blake, Helena & Sultan, Abdul H. [Vis alle 7 forfattere av denne artikkelen] (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 (2017). Overview of Topics Related to Model Selection for Regression. Trends in Mathematics. ISSN 2297-0215. 7, s. 77–82. doi: 10.1007/978-3-319-55639-0_13.
  • 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
  • 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; 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 (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. doi: 10.1007/s00180-015-0642-2.
  • 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; 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. doi: 10.1214/15-ejs1045.
  • 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. doi: 10.1002/sim.6246.
  • 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. doi: 10.1186/1471-2288-14-117.
  • 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. doi: 10.1186/1471-2105-12-49.

Se alle arbeider i Cristin

  • Song, You; Villeneuve, Daniel; Moe, Jannicke; Cao, Yang; Hultman, Maria Thérése & Christou, Maria [Vis alle 21 forfattere av denne artikkelen] (2021). Development of quantitative Adverse Outcome Pathways to facilitate next generation risk assessment of mitochondrial toxicants.
  • De Bin, Riccardo (2021). Topic group 9 ‘High-Dimensional Data”: updates and plans.
  • De Bin, Riccardo (2021). Once upon a time... boosting: How some people betting on an ox are helping us to get right cancer treatments or have safe plane landings.
  • Moe, Jannicke; Cao, Yang; De Bin, Riccardo; Tollefsen, Knut-Erik & Song, You (2021). Quantification of an AOP network for UV radiation by statstical and causal probabilistic modelling.
  • Cao, Yang; Moe, Jannicke; De Bin, Riccardo; Tollefsen, Knut-Erik & Song, You (2021). Piecewise structural equation modeling aided construction of a quantitative adverse outcome pathway network.
  • De Bin, Riccardo (2021). Analysis of high-dimensional data: Opportunities, challenges and goals.
  • De Bin, Riccardo (2021). On the asymptotic behaviour of the variance estimator of a U -statistic.
  • De Bin, Riccardo (2021). On the asymptotic behaviour of the variance estimator of a U -statistic.
  • De Bin, Riccardo (2021). Modelling publication bias and p-hacking.
  • De Bin, Riccardo (2021). “Generalization” from a statistical point of view.
  • De Bin, Riccardo (2020). Influence of single observations on the choice of the penalty parameter in ridge regression.
  • Song, You; Hultman, Maria Thérése; Moe, Jannicke; Petersen, Karina; Rundberget, Jan Thomas & Xie, Li [Vis alle 18 forfattere av denne artikkelen] (2020). The RiskAOP project - Quantitative Adverse Outcome Pathway assisted risk assessment of mitochondrial toxicants.
  • De Bin, Riccardo (2020). Statistical Learning.
  • De Bin, Riccardo (2020). A brief overview on topics related to Statistical Learning.
  • De Bin, Riccardo (2019). Influence of single observations on the choice of the penalty parameter in ridge regression.
  • De Bin, Riccardo (2019). First-hitting-time models for high-dimensional data: A statistical boosting approach.
  • De Bin, Riccardo (2019). Research group “Statistics and Data Science” and my research interests.
  • De Bin, Riccardo (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures.
  • 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 (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 (2018). 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). Meta-analysis and selection for significance.
  • De Bin, Riccardo (2018). Integrated likelihoods in the context of distributed computing.
  • De Bin, Riccardo (2018). On combining clinical and omics data in regression prediction models.
  • 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.
  • 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 (2018). On the equivalence between conditional and random-effects likelihoods in exponential families.
  • De Bin, Riccardo (2017). Detection of influential points as a byproduct of resampling-based variable selection procedures.
  • 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). 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). Extensions of threshold-hitting models to higher dimension.
  • 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. doi: 10.5256/f1000research.9340.r18283.

Se alle arbeider i Cristin

Publisert 17. okt. 2016 15:33 - Sist endret 15. mai 2019 18:19