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Gruppiert nach: Dokumententyp | Veröffentlichungsdatum
Anzahl der Publikationen: 25

Hochschulschrift

Janitza, Silke (2010): Methoden der Veränderungsmessung basierend auf dem Rasch-Modell. Bachelorarbeit, Ludwig-Maximilians-Universität München
[PDF, 561kB]

Zeitschriftenartikel

Boulesteix, Anne-Laure; Janitza, Silke; Hornung, Roman; Probst, Philipp; Busen, Hannah und Hapfelmeier, Alexander (2019): Making complex prediction rules applicable for readers: Current practice in random forest literature and recommendations. In: Biometrical Journal, Bd. 61, Nr. 5: S. 1314-1328

Irlbeck, Thomas; Janitza, Silke; Poros, Balazs; Golebiewski, Monika; Frey, Lorenz; Paprottka, Philipp M.; Silva, Teresa da; Irlbeck, Michael; Böcker, Wolfgang und Weig, Thomas (2018): Quantification of Adipose Tissue and Muscle Mass Based on Computed Tomography Scans: Comparison of Eight Planimetric and Diametric Techniques Including a Step-By-Step Guide. In: European Surgical Research, Bd. 59, Nr. 1-2: S. 23-34

Janitza, Silke; Celik, Ender und Boulesteix, Anne-Laure (2018): A computationally fast variable importance test for random forests for high-dimensional data. In: Advances in Data Analysis and Classification, Bd. 12, Nr. 4: S. 885-915

Janitza, Silke und Hornung, Roman (2018): On the overestimation of random forest's out-of-bag error.
In: PLOS ONE 13(8), e0201904 [PDF, 3MB]

Kneidinger, Nikolaus; Milger, Katrin; Janitza, Silke; Ceelen, Felix; Leuschner, Gabriela; Dinkel, Julien; Koenigshoff, Melanie; Weig, Thomas; Schramm, Rene; Winter, Hauke; Behr, Jürgen und Neurohr, Claus (2017): Lung volumes predict survival in patients with chronic lung allograft dysfunction. In: European Respiratory Journal, Bd. 49, Nr. 4, 1601315

Weig, Thomas; Milger, Katrin; Langhans, Birgit; Janitza, Silke; Sisic, Alma; Kenn, Klaus; Irlbeck, Thomas; Pomschar, Andreas; Johnson, Thorsten; Irlbeck, Michael; Behr, Jürgen; Czerner, Stephan; Schramm, René; Winter, Hauke; Neurohr, Claus; Frey, Lorenz und Kneidinger, Nikolaus (April 2016): Core Muscle Size Predicts Postoperative Outcome in Lung Transplant Candidates. In: Annals of Thoracic Surgery, Bd. 101, Nr. 4: S. 1318-1325

Brettner, Florian; Janitza, Silke; Prüll, Kathrin.; Weninger, Ernst; Mansmann, Ulrich; Küchenhoff, Helmut ORCID logoORCID: https://orcid.org/0000-0002-6372-2487; Jovanovic, Alexander; Pollwein, Bernhard; Chappell, Daniel; Zwissler, Bernhard und Dossow, Vera von (2016): Gender-Specific Differences in Low-Dose Haloperidol Response for Prevention of Postoperative Nausea and Vomiting: A Register-Based Cohort Study.
In: PLOS ONE 11(1), e0146746 [PDF, 673kB]

Janitza, Silke; Tutz, Gerhard und Boulesteix, Anne-Laure (2016): Random forest for ordinal responses: Prediction and variable selection. In: Computational Statistics and Data Analysis, Bd. 96: S. 57-73

Rospleszcz, S.; Janitza, Silke und Boulesteix, Anne-Laure (2016): Categorical variables with many categories are preferentially selected in bootstrap-based model selection procedures for multivariable regression models. In: Biometrical Journal, Bd. 58, Nr. 3: S. 652-673

Dolch, M. E.; Janitza, Silke; Boulesteix, Anne-Laure; Grassmann-Lichtenauer, Carola; Praun, S.; Denzer, W.; Schelling, Gustav und Schubert, S. (2016): Gram-negative and -positive bacteria differentiation in blood culture samples by headspace volatile compound analysis. In: Journal of Biological Research - Thessaloniki 23:3 [PDF, 1MB]

Janitza, Silke; Binder, H. und Boulesteix, Anne-Laure (2016): Pitfalls of hypothesis tests and model selection on bootstrap samples: Causes and consequences in biometrical applications. In: Biometrical Journal, Bd. 58, Nr. 3: S. 447-473

De Bin, Riccardo; Janitza, Silke; Sauerbrei, W. und Boulesteix, Anne-Laure (2015): Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. In: Biometrics, Bd. 72, Nr. 1: S. 272-280

Janitza, Silke; Strobl, Carolin und Boulesteix, Anne-Laure (2013): An AUC-based permutation variable importance measure for random forests. In: BMC Bioinformatics 14:119 [PDF, 296kB]

Hornuss, C.; Dolch, M. E.; Janitza, Silke; Souza, K.; Praun, S.; Apfel, C. C. und Schelling, Gustav (2013): Determination of breath isoprene allows the identification of the expiratory fraction of the propofol breath signal during real-time propofol breath monitoring. In: Journal of Clinical Monitoring and Computing, Bd. 27, Nr. 5: S. 509-516

Paper

Janitza, Silke (10. April 2017): On the overestimation of random forest’s out-of-bag error. Department of Statistics: Technical Reports, Nr. 204 [PDF, 641kB]

Boulesteix, Anne-Laure; Janitza, Silke; Hornung, Roman; Probst, Philipp; Busen, Hannah und Hapfelmeier, Alexander (19. Dezember 2016): Making Complex Prediction Rules Applicable for Readers: Current Practice in Random Forest Literature and Recommendations. Department of Statistics: Technical Reports, Nr. 199 [PDF, 456kB]

Janitza, Silke; Celik, Ender und Boulesteix, Anne-Laure (22. Oktober 2015): A computationally fast variable importance test for random forests for high-dimensional data. Department of Statistics: Technical Reports, Nr. 185 [PDF, 1MB]

Janitza, Silke und Tutz, Gerhard (2. Januar 2015): Prediction Models for Time Discrete Competing Risks. Department of Statistics: Technical Reports, Nr. 177 [PDF, 552kB]

Janitza, Silke; Tutz, Gerhard und Boulesteix, Anne-Laure (1. Dezember 2014): Random Forests for Ordinal Response Data: Prediction and Variable Selection. Department of Statistics: Technical Reports, Nr. 174 [PDF, 597kB]

Janitza, Silke; Binder, Harald und Boulesteix, Anne-Laure (19. November 2014): Pitfalls of hypothesis tests and model selection on bootstrap samples: causes and consequences in biometrical applications. Department of Statistics: Technical Reports, Nr. 163 [PDF, 1MB]

De Bin, Riccardo; Janitza, Silke; Sauerbrei, Willi und Boulesteix, Anne-Laure (14. Oktober 2014): Subsampling versus bootstrapping in resampling-based model selection for multivariable regression. Department of Statistics: Technical Reports, Nr. 171 [PDF, 379kB]

Rospleszcz, Susanne; Janitza, Silke und Boulesteix, Anne-Laure (7. August 2014): Categorical variables with many categories are preferentially selected in model selection procedures for multivariable regression models on bootstrap samples. Department of Statistics: Technical Reports, Nr. 164 [PDF, 604kB]

Janitza, Silke; Strobl, Carolin und Boulesteix, Anne-Laure (November 2012): An AUC-based Permutation Variable Importance Measure for Random Forests. Department of Statistics: Technical Reports, Nr. 130 [PDF, 470kB]

Boulesteix, Anne-Laure; Janitza, Silke; Kruppa, Jochen und König, Inke R. (25. Juli 2012): Overview of Random Forest Methodology and Practical Guidance with Emphasis on Computational Biology and Bioinformatics. Department of Statistics: Technical Reports, Nr. 129 [PDF, 376kB]

Diese Liste wurde am Sat Mar 23 19:56:16 2024 CET erstellt.