Riccardo De Bin

Associate Professor - Statistics and Data Science
Image of Riccardo De Bin
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Email debin@math.uio.no
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
Other affiliations The International Summer School (Student)

SHORT CV

 

RESEARCH INTERESTS

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

 

TEACHING

 

Tags: Statistics, Statistics and biostatistics

Publications

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  • De Bin, Riccardo (2020). A brief overview on topics related to Statistical Learning.
  • De Bin, Riccardo (2020). Statistical Learning.
  • Song, You; Hultman, Maria Thérése; Moe, Jannicke; Petersen, Karina; Rundberget, Jan Thomas; Xie, Li; Cao, Yang; Esguerra, Camila Vicencio; De Bin, Riccardo; Lee, YeonKyeong; Solhaug, Knut Asbjørn; Kamstra, Jorke H; Legler, Juliette; Iguchi, Taisen; Lillicrap, Adam David; Andersen, Sjur; Villeneuve, Daniel L. & Tollefsen, Knut-Erik (2020). The RiskAOP project - Quantitative Adverse Outcome Pathway assisted risk assessment of mitochondrial toxicants.
  • 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

View all works in Cristin

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

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