David Rügamer

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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
Bernard LiewUniversity of Essex, United KingdomVerified email at essex.ac.uk
Alessandro Marco De NunzioLUNEX International University of Health, Exercise and SportsVerified email at lunex-university.net
Professor Deborah FallaChair in Rehabilitation Science and Physiotherapy, School of Sport, Exercise and RehabilitationVerified email at bham.ac.uk
Nadja KleinHumboldt-Universität zu BerlinVerified email at hu-berlin.de
Thomas KneibChair of Statistics, Georg-August-University GöttingenVerified email at uni-goettingen.de
Torsten HothornVerified email at R-project.org
Helmut KüchenhoffInstitut für Statistik, LMU MünchenVerified email at stat.uni-muenchen.de
Gerhard WessLMU TierkardiologieVerified email at lmu.de
Fabian ScheiplLMU Munich / Munich Center for Machine LearningVerified email at stat.uni-muenchen.de
Klaus R. SchererProfessor of Psychology, University of GenevaVerified email at unige.ch
Christopher MutschlerFraunhofer IISVerified email at iis.fraunhofer.de
Sebastian PölsterlArtificial Intelligence in Medical Imaging, Ludwig Maximilian University, MunichVerified email at poelsterl.net
Christian WachingerMIT, Harvard medical schoolVerified email at csail.mit.edu
Christian L. MüllerLudwig-Maximilians-Universität München; Helmholtz Zentrum München; Flatiron Institute, New YorkVerified email at lmu.de
Michael IngrischDepartment of Radiology, Ludwig-Maximilians-University Hospital MunichVerified email at med.uni-muenchen.de
Marius LindauerLeibniz University Hannover (Germany)Verified email at tnt.uni-hannover.de
Kevin NettoCurtin School of Allied Health, Curtin enAble InstituteVerified email at curtin.edu.au
Sven StrickrothProfessor of Computer Science (Technology-Enhanced Learning), LMU Munich, GermanyVerified email at ifi.lmu.de

David Rügamer

Interim Professor for Computational Statistics, RWTH Aachen

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

Computational Statistics Data Science Machine 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	51	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), 1-9, 2014	51	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	38	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	31	2016
Boosting Functional Regression Models with FDboost S Brockhaus, D Rügamer, S Greven Journal of Statistical Software 94 (10), 2020	23	2020
cAIC4: Conditional Akaike information criterion for lme4 B Saefken, D Ruegamer, T Kneib, S Greven R package version 0.3, 2018	20	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	19	2016
Semi-structured deep distributional regression: Combining structured additive models and deep learning D Rügamer, C Kolb, N Klein arXiv preprint arXiv:2002.05777, 2020	17*	2020
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	14	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	13	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	12	2018
Deep Conditional Transformation Models P Baumann, T Hothorn, D Rügamer Machine Learning and Knowledge Discovery in Databases. Research Track. ECML …, 2021	9	2021
Inference for -Boosting D Rügamer, S Greven Statistics and Computing 30 (2), 279-289, 2020	9	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	9	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	8	2020
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), 1-18, 2022	7*	2022
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	6	2020
Neural Mixture Distributional Regression D Rügamer, F Pfisterer, B Bischl arXiv preprint arxiv:2010.06889, 2020	5	2020
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression D Rügamer, C Kolb, C Fritz, F Pfisterer, B Bischl, R Shen, C Bukas, ... Journal of Statistical Software (provisionally accepted), 2022	4	2022
Selective Inference for Additive and Linear Mixed Models D Rügamer, PFM Baumann, S Greven Computational Statistics & Data Analysis 167 (107350), 2022	3	2022

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