Munk, Axel

Authors Pohlmann M, Werner F, Munk A Journal Information and Inference: A Journal of the IMA Citation Information and Inference: A Journal of the IMA, Volume 12, Issue 3, September 2023, iaad026. Abstract We investigate minimax testing for detecting local signals or linear combinations of such signals when only indirect

Authors Mordant G, Munk A Journal SIAM Journal on Mathematics of Data Science Citation IAM Journal on Mathematics of Data Science 2023 5:2, 528-557 Abstract In this paper, we consider a certain convolutional Laplacian for metric measure spaces and investigate its potential for the statistical analysis of complex objects. The

Authors Mösching A, Li H, Munk A Journal ArXiv Citation arXiv:2305.18578. Abstract Hidden Markov models (HMMs) are characterized by an unobservable (hidden) Markov chain and an observable process, which is a noisy version of the hidden chain. Decoding the original signal (i.e., hidden chain) from the noisy observations is one

Authors Laitenberger O, Aspelmeier T, Staudt T, Geisler C, Munk A, Egner A Journal Nanomaterials Citation Nanomaterials 2023, 13(3), 459. Abstract With the advent of fluorescence superresolution microscopy, nano-sized structures can be imaged with a previously unprecedented accuracy. Therefore, it is rapidly gaining importance as an analytical tool in the

Authors Heinemann F, Klatt M, Munk A Journal Applied Mathematics & Optimization Citation Appl Math Optim 87, 4 (2023). Abstract The purpose of this paper is to provide a systematic discussion of a generalized barycenter based on a variant of unbalanced optimal transport (UOT) that defines a distance between general

Authors Kovács S, Li H, Bühlmann P, Munk A Journal Biometrika Citation Biometrika, 2022, asac052. Abstract We propose seeded binary segmentation for large scale changepoint detection problems. We construct a deterministic set of background intervals, called seeded intervals, in which single changepoint candidates are searched for. The final selection of

Authors Weitkamp CA, Proksch K, Tameling C, Munk A Journal Journal of the American Statistical Association Citation J Am Stat Assoc. 1-32. 2022. Abstract In this paper, we aim to provide a statistical theory for object matching based on a lower bound of the Gromov-Wasserstein distance related to the distribution

Authors Hundrieser S, Klatt M, Munk A Journal Directional Statistics for Innovative Applications Citation SenGupta, A., Arnold, B.C. (eds) Directional Statistics for Innovative Applications. Forum for Interdisciplinary Mathematics. Springer, Singapore. Abstract Empirical optimal transport (OT) plans and distances provide effective tools to compare and statistically match probability measures defined on

Authors Behr M, Munk A Journal SIAM Journal on Mathematics of Data Science Citation SIAM Journal on Mathematics of Data Science 4(2):490-513. Abstract We provide a minimax optimal estimation procedure for F and W in matrix valued linear models Y = F W + Z where the parameter matrix W

Authors Haltmeier M, Li H, Munk A Journal Annual Review of Statistics and Its Application Citation Annu. Rev. Stat. Appl. 2022.9:343-372. Abstract We present a unifying view on various statistical estimation techniques including penalization, variational, and thresholding methods. These estimators are analyzed in the context of statistical linear inverse problems