Munk, Axel

Authors Li H, Munk A, Sieling H, Walther G Journal Biometrika Citation Biometrika (2020), 107, 2, pp. 347–364 Abstract The histogram is widely used as a simple, exploratory way of displaying data, but it is usually not clear how to choose the number and size of the bins. We construct

Authors Behr M, Ansari MA, Munk A , Holmes C Journal Proceedings of the National Academy of Sciences of the United States of America Citation PNAS May 5, 2020 117 (18) 9787-9792. Abstract Tree structures, showing hierarchical relationships and the latent structures between samples, are ubiquitous in genomic and biomedical

Authors Staudt T, Aspelmeier T, Laitenberger O, Geisler C, Egner A, Munk A Journal Statistical Science Citation Statist. Sci., Volume 35, Number 1 (2020), 92-111. Abstract Super-resolution microscopy is rapidly gaining importance as an analytical tool in the life sciences. A compelling feature is the ability to label biological units

Authors Frahm L, Keller-Findeisen J, Alt P, Schnorrenberg S, Del Alamo Ruiz M, Aspelmeier T, Munk A, Jakobs S, Hell SW Journal Optics Express Citation Opt Express. 2019 Jul 22;27(15):21956-21987. Abstract BACKGROUND: The ultimate objective of a microscope of the highest resolution is to map the molecules of interest in

Authors Bartsch A, Llabres S, Pein F, Kattner C, Schon M, Diehn M, Tanabe M, Munk A, Zachariae U, Steinem C Journal Scientific Reports Citation Sci Rep. 2019 Feb 4;9(1):1264. Abstract The permeation of most antibiotics through the outer membrane of Gram-negative bacteria occurs through porin channels. To design drugs

Authors Li H, Guo Q, Munk A Journal Electronic Journal of Statistics Citation Electron. J. Statist. Volume 13, Number 2 (2019), 3254-3296. Abstract Modern multiscale type segmentation methods are known to detect multiple change-points with high statistical accuracy, while allowing for fast computation. Underpinning (minimax) estimation theory has been developed

Authors Tameling C, Sommerfeld M, Munk A Journal Annals of Applied Probability Citation Ann. Appl. Probab. 29 (2019), no. 5, 2744–2781. Abstract We derive distributional limits for empirical transport distances between probability measures supported on countable sets. Our approach is based on sensitivity analysis of optimal values of infinite dimensional