Publikationen von Axel Munk

Pohlmann, M.; Werner, F.; Munk, A.: Minimax detection of localized signals in statistical inverse problems. Information and Inference 12 (3), S. 2160 - 2196 (2023)

Weitkamp, C. A.; Proksch, K.; Tameling, C.; Munk, A.: Distribution of Distances based Object Matching: Asymptotic Inference. Journal of the American Statistical Association, in press (2022)

Heinemann, F.; Klatt, M.; Munk, A.: Kantorovich-Rubinstein Distance and Barycenter for Finitely Supported Measures: Foundations and Algorithms. Applied Mathematics & Optimization 87 (1), 4 (2022)

Heinemann, F.; Munk, A.; Zemel, Y.: Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees. SIAM Journal on Mathematics of Data Science 4 (1), S. 229 - 259 (2022)

Behr, M.; Munk, A.: Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets. SIAM Journal on Mathematics of Data Science 4 (2), S. 490 - 513 (2022)

Pein, F.; Eltzner, B.; Munk, A.: Analysis of patchclamp recordings: model-free multiscale methods and software. European Biophysics Journal 50 (2), S. 187 - 209 (2021)

Li, H.; Guo, Q.; Munk, A.: Multiscale change-point segmentation: beyond step functions. Electronic Journal of Statistics 13 (2), S. 3254 - 3296 (2019)

Diehn, M.; Munk, A.; Rudolf, D.: Maximum likelihood estimation in hidden Markov models with inhomogeneous noise. ESAIM: Probability and Statistics 23, S. 492 - 523 (2019)

Frahm, L.; Keller, J.; Alt, P.; Schnorrenberg, S.; Ruiz, M. D.; Aspelmeier, T.; Munk, A.; Jakobs, S.; Hell, S. W.: Molecular contribution function in RESOLFT nanoscopy. Optics Express 27 (15), S. 21956 - 21987 (2019)

Sommerfeld, M.; Schrieber, J.; Zemel, Y.; Munk, A.: Optimal transport: Fast probabilistic approximation with exact solvers. The Journal of Machine Learning Research 20, (in press) (2019)

Bartsch, A.; Llabrés, S.; Pein, F.; Kattner, C.; Schön, M.; Diehn, M.; Tanabe, M.; Munk, A.; Zachariae, U.; Steinem, C.: High-resolution experimental and computational electrophysiology reveals weak β-lactam binding events in the porin PorB. Scientific Reportsvolume 9 (1), 1264 (2019)

Pein, F.; Tecuapetla-Gómez, I.; Schütte, O. M.; Steinem, C.; Munk, A.: Fully-automatic multiresolution idealization for filtered ion channel recordings: Flickering event detection. IEEE Transactions on NanoBioscience 17 (3), S. 300 - 320 (2018)

Enikeeva, F.; Munk, A.; Werner, F.: Bump detection in heterogeneous Gaussian regression. Bernoulli 24 (2), S. 1266 - 1306 (2018)

Grasmair, M.; Li, H.; Munk, A.: Variational multiscale nonparametric regression: Smooth functions. Annales de l'Institut Henri Poincaré, Probabilités et Statistiques 54 (2), S. 1058 - 1097 (2018)

Behr, M.; Holmes, C.; Munk, A.: Multiscale blind source separation. The Annals of Statistics 46 (2), S. 711 - 744 (2018)

Proksch, K.; Werner, F.; Munk, A.: Multiscale scanning in inverse problems. The Annals of Statistics 46 (6B), S. 3569 - 3602 (2018)

Sommerfeld, M.; Munk, A.: Inference for empirical Wasserstein distances on finite spaces. Journal of the Royal Statistical Society. Ser. B, Statistical Methodology 80 (1), S. 219 - 238 (2018)

Behr, M.; Munk, A.: Identifiability for blind source separation of multiple finite alphabet linear mixtures. IEEE Transactions on Information Theory 63 (9), S. 5506 - 5517 (2017)

Pein, F.; Sieling, H.; Munk, A.: Heterogeneous change point inference. Journal of the Royal Statistical Society. Series B, Statistical Methodology 79 (4), S. 1207 - 1227 (2017)

Tecuapetla-Gomez, I.; Munk, A.: Autocovariance estimation in regression with a discontinuous signal and m-dependent errors: A difference-based approach. Scandinavian Journal of Statistics 44 (2), S. 346 - 368 (2017)