David Rügamer

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Citations	737	687
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i10-index	23	22

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Bernd BischlChair of Statistical Learning and Data Science, LMU MunichVerified email at stat.uni-muenchen.de
Sonja GrevenChair of Statistics, Humboldt-Universität zu BerlinVerified email at hu-berlin.de
Christopher MutschlerFraunhofer IISVerified email at iis.fraunhofer.de
Thomas KneibChair of Statistics, Georg-August-University GöttingenVerified email at uni-goettingen.de
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Torsten HothornVerified email at R-project.org
Fabian ScheiplLMU Munich / Munich Center for Machine LearningVerified email at stat.uni-muenchen.de
Christian L. MüllerHelmholtz Munich; Ludwig-Maximilians-Universität München; Flatiron Institute, New YorkVerified email at helmholtz-munich.de
Lucas KookPostdoctoral Fellow, University of CopenhagenVerified email at wu.ac.at
Christian WachingerMIT, Harvard medical schoolVerified email at csail.mit.edu
Almond StöckerPostDoc, École Polytechnic Fédéral de Lausanne (EPFL)Verified email at epfl.ch
Stephan GünnemannProfessor of Computer Science, Technical University of MunichVerified email at in.tum.de
Marius LindauerLeibniz University Hannover (Germany), Institute of Artificial Intelligence LUH|AI, L3S ResearchVerified email at ai.uni-hannover.de
Oliver DürrProfessor of Data Science, Konstanz University of Applied SciencesVerified email at htwg-konstanz.de
Bettina GrünWirtschaftsuniversität WienVerified email at wu.ac.at
Theodore PapamarkouThe University of ManchesterVerified email at manchester.ac.uk

David Rügamer

Other namesDavid Ruegamer, David Rugamer

Associate Professor for Data Science, LMU Munich

Verified email at stat.uni-muenchen.de - Homepage

Computational Statistics Data Science Machine Learning Deep Learning


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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	82	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	62	2014
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	51*	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	47	2016
Boosting Functional Regression Models with FDboost S Brockhaus, D Rügamer, S Greven Journal of Statistical Software 94 (10), 2020	38	2020
Semi-structured Distributional Regression D Rügamer, C Kolb, N Klein The American Statistician, 1-25, 2023	34*	2023
FDboost: boosting functional regression models S Brockhaus, D Rügamer R package version 0.2-0, URL https://CRAN. R-project. org/package= FDboost, 2016	30	2016
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	28	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	27	2016
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	24	2022
cAIC4: Conditional Akaike information criterion for lme4 B Saefken, D Ruegamer, T Kneib, S Greven R package version 0.3, 2018	23	2018
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	20	2020
Deep Conditional Transformation Models P Baumann, T Hothorn, D Rügamer Machine Learning and Knowledge Discovery in Databases. Research Track. ECML …, 2021	19	2021
Inference for -Boosting D Rügamer, S Greven Statistics and Computing 30 (2), 279-289, 2020	18	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	16	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	15	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	15	2018
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	13	2021
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	13	2020
Domain Adaptation for Time-Series Classification to Mitigate Covariate Shift F Ott, D Rügamer, L Heublein, B Bischl, C Mutschler Proceedings of the 30th ACM International Conference on Multimedia, 2022	11	2022

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