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

Se alle arbeider i Cristin

  • 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 (2021). On the asymptotic behaviour of the variance estimator of a U -statistic.
  • De Bin, Riccardo (2021). Analysis of high-dimensional data: Opportunities, challenges and goals.
  • 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.
  • 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.
  • 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). A brief overview on topics related to Statistical Learning.
  • De Bin, Riccardo (2020). Statistical Learning.
  • De Bin, Riccardo (2020). Influence of single observations on the choice of the penalty parameter in ridge regression.
  • De Bin, Riccardo (2019). Statistics meets machine learning: the example of statistical boosting.
  • De Bin, Riccardo (2019). Detection of influential points as a byproduct of resampling-based variable selection procedures.
  • De Bin, Riccardo (2019). Research group “Statistics and Data Science” and my research interests.
  • 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 (2019). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
  • 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 (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). On the equivalence between conditional and random-effects likelihoods in exponential families.
  • 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). 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). Strategies to derive combined prediction models using both clinical predictors and high-throughput molecular data.
  • De Bin, Riccardo (2018). Integrated likelihoods in the context of distributed computing.
  • De Bin, Riccardo (2018). Strategies to handle mandatory covariates using model- and likelihood-based boosting.
  • De Bin, Riccardo (2018). On combining clinical and omics data in regression prediction models.
  • 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). 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). 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. doi: 10.5256/f1000research.9340.r18283

Se alle arbeider i Cristin

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