David Rügamer
David Rügamer
Other namesDavid Ruegamer, David Rugamer
Professor of Data Science (LMU Munich, Munich Center for Machine Learning)
Verified email at - Homepage
Cited by
Cited by
Conditional model selection in mixed-effects models with caic4
B Säfken, D Rügamer, T Kneib, S Greven
Journal of Statistical Software 99 (8), 1-30, 2021
Combining graph neural networks and spatio-temporal disease models to improve the prediction of weekly COVID-19 cases in Germany
C Fritz, E Dorigatti, D Rügamer
Scientific Reports 12 (1), 3930, 2022
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, 1-9, 2014
Machine learning versus logistic regression for prognostic modelling in individuals with non-specific neck pain
BXW Liew, FM Kovacs, D Rügamer, A Royuela
European Spine Journal 31 (8), 2082-2091, 2022
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
Boosting Functional Regression Models with FDboost
S Brockhaus, D Rügamer, S Greven
Journal of Statistical Software 94 (10), 2020
Semi-structured Distributional Regression
D Rügamer, C Kolb, N Klein
The American Statistician, 1-25, 2023
FDboost: Boosting functional regression models
S Brockhaus, D Rügamer, A Stöcker
R package version 0.2-0, URL https://CRAN. R-project. org/package= FDboost, 2016
A General Machine Learning Framework for Survival Analysis
A Bender, D Rügamer, F Scheipl, B Bischl
ECML-PKDD 2020, 2020
Boosting factor-specific functional historical models for the detection of synchronisation in bioelectrical signals
D Rügamer, S Brockhaus, K Gentsch, K Scherer, S Greven
Journal of Royal Statistical Society: Series C, 2016
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
Deep Conditional Transformation Models
P Baumann, T Hothorn, D Rügamer
ECML-PKDD 2021 12977, 2021
Semi-Structured Deep Piecewise Exponential Models
P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer
AAAI 2020, Spring Symposium on Survival Prediction -- Algorithms, Challenges …, 2020
cAIC4: Conditional Akaike information criterion for lme4
B Saefken, D Ruegamer, T Kneib, S Greven
R package version 0.3, 2018
Selective inference after likelihood- or test-based model selection in linear models
D Rügamer, S Greven
Statistics & Probability Letters 140 (C), 7-12, 2018
Deep Semi-Supervised Learning for Time Series Classification
J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl
ICMLA 2021, 2021
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), 16782, 2020
Inference for -Boosting
D Rügamer, S Greven
Statistics and Computing 30 (2), 279-289, 2020
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift
F Ott, D Rügamer, L Heublein, B Bischl, C Mutschler
ACM MM 2022, 2022
DeepPAMM: Deep Piecewise Exponential Additive Mixed Models for Complex Hazard Structures in Survival Analysis
P Kopper, S Wiegrebe, B Bischl, A Bender, D Rügamer
PAKDD 2022, 249-261, 2022
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