Publikationen

Referred articles:

• De Bin, R. (2016). Boosting in Cox regression: a comparison between the likelihood-based and the model-based approaches with focus on the R-packages CoxBoost and mboost. Computational Statistics, DOI:10.1007/s00180-015-0642-2
  
• De Bin, R. (2016). A note on the equivalence between conditional and random-effects likelihoods in exponential families. Statistics & Probability Letters 110: 34–38
  
• De Bin, R., Janitza, S., Sauerbrei, W. & Boulestiex A. L. (2015). Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Biometrics, DOI: 10.1111/biom.12381
• De Bin, R., Severini, T. A. & Sartori N. (2015). Integrated likelihoods in models with stratum nuisance parameters. Electronic Journal of Statistics 7:1474-1491
• De Bin, R., Sauerbrei, W. & Boulestiex A. L. (2014). Investigating the prediction ability of survival models based on both clinical and omics data: two case studies. Statistics in Medicine 33:5310-5329
• De Bin, R., Herold, T. & Boulesteix, A. L. (2014). Added predictive value of omics data: specific issues related to validation illustrated by two case studies. BMC Medical Research Methodology 14:117
• De Bin, R. & Risso D. (2011). A novel approach to the clustering of microarray data via nonparametric density estimation. BMC Bioinformatics 12:49 

Conference proceedings:

• De Bin, R., Doerken, S., Boulesteix, A. L. & Sauerbrei, W. (2014). Graphical tools for investigating variable selection instability caused by correlated variables. 26th International Biometric Conference Meeting, ISBN 978 0 9821919 3 4
• De Bin R. & Boulesteix A. L. (2012). Added predictive value of prediction models based on high-dimensional data. 5th International Conference of the European Research Consortium for Informatics and Mathematics, 2012, ISBN 978 84 937822 2 1
• De Bin R. & Risso D. (2010). A nonparametric algorithm for clustering microarray data. 45th Scientific Meeting of the Italian Statistical Society, 2010, ISBN 978 88 6129 566 7
• De Bin R. & Risso D. (2010). Clustering via nonparametric density estimation: an application to microarray data. LASR 2010 — High-throughput sequencing, proteins and statistics ISBN 978 0 85316 293 3

Technical reports:

• Seibold H., Bernau C., Boulesteix, A. L. & De Bin, R. (2016). On the choice and influence of the number of boosting steps. Technical Report 188, Department of Statistics, University of Munich
• Boulesteix, A. L., De Bin, R., Jiang, X. & Fuchs, M. (2015). IPF-LASSO: integrative L1-penalized regression with penalty factors for prediction based on multi-omics data. Technical Reports 187, Department of Statistics, University of Munich
  
• De Bin, R. & Scarpa, B. (2014). Non-parametric Bayesian modeling of cervical mucus symptom. Technical Report 170, Department of Statistics, University of Munich
• Fuchs, M., Hornung, R., De Bin, R. & Boulesteix, A. L. (2013). A U-statistic estimator for the variance of resampling-based error estimators. Technical Report 148, Department of Statistics, University of Munich

Other manuscripts (submitted):

• Doerken, S., De Bin, R. & Sauerbrei, W. Graphical tools for illustrating variable selection (in)stability.