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Song, You; Lillicrap, Adam David; Esguerra, Camila Vicencio; Villeneuve, Daniel L.; Ng, Daniel & Rundberget, Jan Thomas
[Vis alle 15 forfattere av denne artikkelen]
(2022).
Quantitative Adverse Outcome Pathways Assisted Next Generation Risk Assessment of Mitochondrial Toxicants.
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De Bin, Riccardo
(2022).
A boosting model for survival analysis with dependent censoring.
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De Bin, Riccardo
(2022).
First-hitting-time models for high-dimensional data: A statistical boosting approach.
-
De Bin, Riccardo
(2022).
A boosting first hitting time model for survival analysis in high-dimensional settings.
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Hubin, Aliaksandr & De Bin, Riccardo
(2022).
On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials.
-
Hubin, Aliaksandr & De Bin, Riccardo
(2022).
On a genetically modified mode jumping MCMC approach for multivariate fractional polynomials.
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Song, You; Villeneuve, Daniel L.; 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.
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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.
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De Bin, Riccardo
(2021).
Topic group 9 ‘High-Dimensional Data”: updates and plans.
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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.
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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.
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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.
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De Bin, Riccardo
(2021).
Analysis of high-dimensional data: Opportunities, challenges and goals.
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De Bin, Riccardo
(2021).
On the asymptotic behaviour of the variance estimator of a U -statistic.
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De Bin, Riccardo
(2021).
On the asymptotic behaviour of the variance estimator of a U -statistic.
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De Bin, Riccardo
(2021).
Modelling publication bias and p-hacking.
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De Bin, Riccardo
(2021).
“Generalization” from a statistical point of view.
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De Bin, Riccardo
(2020).
Influence of single observations on the choice of the penalty parameter in ridge regression.
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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.
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De Bin, Riccardo
(2020).
Statistical Learning.
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De Bin, Riccardo
(2020).
A brief overview on topics related to Statistical Learning.
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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.
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De Bin, Riccardo
(2019).
Detection of influential points as a byproduct of resampling-based variable selection procedures.
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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.
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De Bin, Riccardo
(2018).
Meta-analysis and selection for significance.
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De Bin, Riccardo
(2018).
Integrated likelihoods in the context of distributed computing.
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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.
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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.
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De Bin, Riccardo
(2018).
On the equivalence between conditional and random-effects likelihoods in exponential families.
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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.