David Rügamer
David Rügamer
Interim Professor for Computational Statistics, TU Dortmund
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
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
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
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
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
cAIC4: Conditional Akaike information criterion for lme4
B Saefken, D Ruegamer, T Kneib, S Greven
R package version 0.3, 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
Journal of Royal Statistical Society: Series C, 2016
Deep Conditional Transformation Models
P Baumann, T Hothorn, D Rügamer
Machine Learning and Knowledge Discovery in Databases. Research Track. ECML …, 2021
A General Machine Learning Framework for Survival Analysis
A Bender, D Rügamer, F Scheipl, B Bischl
Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), 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
Selective inference after likelihood- or test-based model selection in linear models
D Rügamer, S Greven
Statistics & Probability Letters 140 (C), 7-12, 2018
Inference for -Boosting
D Rügamer, S Greven
Statistics and Computing 30 (2), 279-289, 2020
Semi-Structured Deep Piecewise Exponential Models
P Kopper, S Pölsterl, C Wachinger, B Bischl, A Bender, D Rügamer
Proceedings of AAAI Spring Symposium on Survival Prediction -- Algorithms …, 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
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
Deep Semi-Supervised Learning for Time Series Classification
J Goschenhofer, R Hvingelby, D Rügamer, J Thomas, M Wagner, B Bischl
20th IEEE International Conference on Machine Learning and Applications (ICMLA), 2021
Benchmarking Online Sequence-to-Sequence and Character-based Handwriting Recognition from IMU-Enhanced Pens
F Ott, D Rügamer, L Heublein, T Hamann, J Barth, B Bischl, C Mutschler
International Journal on Document Analysis and Recognition (IJDAR), 2022
Selective Inference for Additive and Linear Mixed Models
D Rügamer, PFM Baumann, S Greven
Computational Statistics & Data Analysis 167 (107350), 2022
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