Suchergebnisse

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)

Mütze, T.; Konietschke, F.; Munk, A.; Friede, T.: A studentized permutation test for three-arm trials in the 'gold standard' design. Statistics in Medicine 36 (6), S. 883 - 898 (2017)

Bauer, U.; Munk, A.; Sieling, H.; Wardetzky, M.: Persistence barcodes versus Kolmogorov signatures: Detecting modes of one-dimensional signals. Foundations of Computational Mathematics 17 (1), S. 1 - 33 (2017)

Huckemann, S.; Kim, K. R.; Munk, A.; Rehfeld, F.; Sommerfeld , M.; Weickert, J.; Wollnik, C.: The circular SiZer, inferred persistence of shape parameters and application to early stem cell differentiation. Bernoulli 22 (4), S. 2113 - 2142 (2016)

Rippl, T.; Munk, A.; Sturm, A.: Limit laws of the empirical Wasserstein distance: Gaussian distributions. Journal of Multivariate Analysis 151, S. 90 - 109 (2016)

Hartmann, A.; Huckemann, S.; Dannemann, J.; Laitenberger, O.; Geisler, C.; Egner, A.; Munk, A.: Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (3), S. 563 - 587 (2016)