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Riccardo De Bin

Showing results (1-10 of 20) with videos related to

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BMC Bioinformatics|February 10, 2011
A novel approach to the clustering of microarray data via nonparametric density estimationRiccardo De Bin, Davide Risso
Biometrics|September 12, 2021
Modelling publication bias and p-hackingJonas Moss, Riccardo De Bin
Lifetime Data Analysis|April 27, 2022
A boosting first-hitting-time model for survival analysis in high-dimensional settingsRiccardo De Bin, Vegard Grødem Stikbakke
BMC Medical Research Methodology|October 30, 2014
Added predictive value of omics data: specific issues related to validation illustrated by two case studiesRiccardo De Bin, Tobias Herold, Anne-Laure Boulesteix
Statistics in Medicine|July 22, 2014
Investigating the prediction ability of survival models based on both clinical and omics data: two case studiesRiccardo De Bin, Willi Sauerbrei, Anne-Laure Boulesteix
BMC Bioinformatics|July 4, 2020
Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression modelsShaima Belhechmi, Riccardo De Bin, Federico Rotolo, et al.
Biometrics|August 20, 2015
Subsampling versus bootstrapping in resampling-based model selection for multivariable regressionRiccardo De Bin, Silke Janitza, Willi Sauerbrei, et al.
Computational and Mathematical Methods in Medicine|May 27, 2017
IPF-LASSO: Integrative <i>L</i><sub>1</sub>-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics DataAnne-Laure Boulesteix, Riccardo De Bin, Xiaoyu Jiang, et al.
BMC Medical Research Methodology|July 26, 2019
A plea for taking all available clinical information into account when assessing the predictive value of omics dataAlexander Volkmann, Riccardo De Bin, Willi Sauerbrei, et al.
Statistics in Medicine|October 22, 2020
Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resamplingChristine Wallisch, Daniela Dunkler, Geraldine Rauch, et al.
Pageof 2

Showing results (1-10 of 20) with videos related to

Sort By:
Pageof 2
BMC Bioinformatics|February 10, 2011
A novel approach to the clustering of microarray data via nonparametric density estimationRiccardo De Bin, Davide Risso
Biometrics|September 12, 2021
Modelling publication bias and p-hackingJonas Moss, Riccardo De Bin
Lifetime Data Analysis|April 27, 2022
A boosting first-hitting-time model for survival analysis in high-dimensional settingsRiccardo De Bin, Vegard Grødem Stikbakke
BMC Medical Research Methodology|October 30, 2014
Added predictive value of omics data: specific issues related to validation illustrated by two case studiesRiccardo De Bin, Tobias Herold, Anne-Laure Boulesteix
Statistics in Medicine|July 22, 2014
Investigating the prediction ability of survival models based on both clinical and omics data: two case studiesRiccardo De Bin, Willi Sauerbrei, Anne-Laure Boulesteix
BMC Bioinformatics|July 4, 2020
Accounting for grouped predictor variables or pathways in high-dimensional penalized Cox regression modelsShaima Belhechmi, Riccardo De Bin, Federico Rotolo, et al.
Biometrics|August 20, 2015
Subsampling versus bootstrapping in resampling-based model selection for multivariable regressionRiccardo De Bin, Silke Janitza, Willi Sauerbrei, et al.
Computational and Mathematical Methods in Medicine|May 27, 2017
IPF-LASSO: Integrative <i>L</i><sub>1</sub>-Penalized Regression with Penalty Factors for Prediction Based on Multi-Omics DataAnne-Laure Boulesteix, Riccardo De Bin, Xiaoyu Jiang, et al.
BMC Medical Research Methodology|July 26, 2019
A plea for taking all available clinical information into account when assessing the predictive value of omics dataAlexander Volkmann, Riccardo De Bin, Willi Sauerbrei, et al.
Statistics in Medicine|October 22, 2020
Selection of variables for multivariable models: Opportunities and limitations in quantifying model stability by resamplingChristine Wallisch, Daniela Dunkler, Geraldine Rauch, et al.
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