Riccardo De Bin

Associate Professor - Statistics and Data Science
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Norwegian version of this page
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

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.
  • 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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