Prof. Dr. Maximilian Coblenz
Fachbereich III - Dienstleistungen und Consulting, Hochschule für Wirtschaft und Gesellschaft Ludwigshafen
Kächele, Fabian; Coblenz, Maximilian; Grothe, Oliver
A comparison of latent space modeling techniques in a plain-vanilla autoencoder settingMachine Learning. Bd. 114. H. 7. Springer Science and Business Media LLC 2025 151
Liu, Bolin; Coblenz, Maximilian; Grothe, Oliver
Copula-based Probabilistic Prediction of Grid Frequency DynamicsProceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems. New York, NY, USA: ACM 2025 S. 733 - 741
Liu, Bolin; Coblenz, Maximilian; Grothe, Oliver
Predicting grid frequency short-term dynamics with Gaussian processes and sequence modelingProceedings of the 15th ACM International Conference on Future and Sustainable Energy Systems. New York, NY, USA: ACM 2024 S. 535 - 550
Holz, Simon; Coblenz, Maximilian; Koch, Rainer et al.
Two approaches for constructing multivariate injection models for prefilming airblast atomizersInternational Journal of Multiphase Flow. Bd. 181. Elsevier BV 2024 104999
Mohr, Robert; Coblenz, Maximilian; Kirst, Peter
Globally optimal univariate spline approximationsComputational Optimization and Applications. Bd. 85. H. 2. Springer Science and Business Media LLC 2023 S. 409 - 439
Coblenz, Maximilian; Grothe, Oliver; Herrmann, Klaus et al.
Smooth bootstrapping of copula functionalsElectronic Journal of Statistics. Bd. 16. H. 1. Institute of Mathematical Statistics 2022
Coblenz, Maximilian
MATVines: A vine copula package for MATLABSoftwareX. Bd. 14. Elsevier BV 2021 S. 100700
Coblenz, Maximilian; Holz, Simon; Bauer, Hans-Jörg et al.
Modelling Fuel Injector Spray Characteristics in Jet Engines by Using Vine CopulasJournal of the Royal Statistical Society Series C: Applied Statistics. Bd. 69. H. 4. Oxford University Press (OUP) 2020 S. 863 - 886
Coblenz, Maximilian
Advances in Dependence Modeling: Multivariate Quantiles, Copula Level Curve Lengths, and Non-Simplified Vine CopulasKarlsruhe: Karlsruher Institut für Technologie (KIT) 2018 172 S., Zugl.: Dissertation, Karlsruher Institut für Technologie (KIT), 2018
Coblenz, Maximilian; Dyckerhoff, Rainer; Grothe, Oliver
Confidence Regions for Multivariate QuantilesWater. Bd. 10. H. 8. MDPI AG 2018 S. 996