David Rügamer
David Rügamer
Postdoctoral Researcher, LMU Munich
Bestätigte E-Mail-Adresse bei stat.uni-muenchen.de - Startseite
Zitiert von
Zitiert von
Giardiosis and other enteropathogenic infections: a study on diarrhoeic calves in Southern Germany
J Gillhuber, D Rügamer, K Pfister, MC Scheuerle
BMC research notes 7 (1), 1-9, 2014
Predictors of sudden cardiac death in doberman pinschers with dilated cardiomyopathy
L Klüser, PJ Holler, J Simak, G Tater, P Smets, D Rügamer, H Küchenhoff, ...
Journal of veterinary internal medicine 30 (3), 722-732, 2016
FDboost: boosting functional regression models
S Brockhaus, D Rügamer
R package version 0.2-0, URL https://CRAN. R-project. org/package= FDboost, 2016
Conditional model selection in mixed-effects models with caic4
B Säfken, D Rügamer, T Kneib, S Greven
to appear in the Journal of Statistical Software, 2018
Boosting factor-specific functional historical models for the detection of synchronisation in bioelectrical signals
D Rügamer, S Brockhaus, K Gentsch, K Scherer, S Greven
arXiv preprint arXiv:1609.06070, 2016
cAIC4: Conditional Akaike information criterion for lme4
B Saefken, D Ruegamer, T Kneib, S Greven
R Packag. version 0.2. https://cran. r-project. org/package= cAIC4. Accessed …, 2018
Boosting Functional Regression Models with FDboost
S Brockhaus, D Rügamer, S Greven
Journal of Statistical Software 94 (10), 2020
Selective inference after likelihood- or test-based model selection in linear models
D Rügamer, S Greven
Statistics & Probability Letters 140 (C), 7-12, 2018
Package ‘FDboost’
S Brockhaus, D Ruegamer, T Hothorn, MS Brockhaus
A Unified Network Architecture for Semi-Structured Deep Distributional Regression
D Rügamer, C Kolb, N Klein
arXiv e-prints, arXiv: 2002.05777, 2020
A general machine learning framework for survival analysis
A Bender, D Rügamer, F Scheipl, B Bischl
arXiv preprint arXiv:2006.15442, 2020
Inference for -Boosting
D Rügamer, S Greven
Statistics and Computing 30 (2), 279-289, 2020
Deep Conditional Transformation Models
P Baumann, T Hothorn, D Rügamer
arXiv preprint arxiv:2010.07860, 2020
Neural Mixture Distributional Regression
D Rügamer, F Pfisterer, B Bischl
arXiv preprint arxiv:2010.06889, 2020
Classifying neck pain status using scalar and functional biomechanical variables–development of a method using functional data boosting
BXW Liew, D Rugamer, A Stocker, AM De Nunzio
Gait & posture 76, 146-150, 2020
Deep Semi-Supervised Learning for Time Series Classification
J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl
arXiv preprint arXiv:2102.03622, 2021
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
C Fritz, E Dorigatti, D Rügamer
arXiv preprint arXiv:2101.00661, 2021
Semi-Structured Deep Piecewise Exponential Models
P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer
https://arxiv.org/abs/2011.05824, 2020
Clinical predictive modelling of post-surgical recovery in individuals with cervical radiculopathy: a machine learning approach
BXW Liew, A Peolsson, D Rugamer, J Wibault, H Löfgren, A Dedering, ...
Scientific reports 10 (1), 1-10, 2020
Interpretable machine learning models for classifying low back pain status using functional physiological variables
BXW Liew, D Rugamer, AM De Nunzio, D Falla
European Spine Journal 29 (8), 1845-1859, 2020
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