Munk, Axel

Authors Mösching A, Li H, Munk A Journal Journal of Computational and Graphical Statistics Citation Journal of Computational and Graphical Statistics, 1–15. Abstract Hidden Markov models (HMMs) are characterized by an unobservable Markov chain and an observable process—a noisy version of the hidden chain. Decoding the original signal from the

Authors Suchan L, Li H, Munk A Journal Electronic Journal of Statistics Citation Electron. J. Statist. 19(2): 5925-5978 (2025). Abstract Graph cuts are among the most prominent tools for clustering and classification analysis. While intensively studied from geometric and algorithmic perspectives, graph cut-based statistical inference still remains elusive to a

Authors Hüselitz A, Li H, Munk A Journal Journal of Time Series Analysis Citation Journal of Time Series Analysis 1–22. Abstract We consider the problem of sequential (online) estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that certain CUSUM-type statistics

Authors Requadt R, Li H, Munk A Journal Arxiv Citation arXiv:2510.05645. Abstract We investigate the asymptotic behavior of parametric Bayes estimators under a broad class of loss functions that extend beyond the classical translation-invariant setting. To this end, we develop a unified theoretical framework for loss functions exhibiting locally polynomial

Authors Groppe M, Niemöller L, Hundrieser S, Ventzke D, Blob A, Köster S, Munk A Journal Arxiv Citation arXiv:2509.13970. Abstract We introduce a general framework for testing statistical hypotheses for probability measures supported on finite spaces, which is based on optimal transport (OT). These tests are inspired by the analysis

Authors Struleva M, Hundrieser S, Schuhmacher D, Munk A Journal Arxiv Citation arXiv:2509.04225. Abstract We statistically analyze empirical plug-in estimators for unbalanced optimal transport (UOT) formalisms, focusing on the Kantorovich-Rubinstein distance, between general intensity measures based on observations from spatio-temporal point processes. Specifically, we model the observations by two weakly

Authors Hundrieser S, Manole T, Litskevich D, Munk A Journal Arxiv Citation arXiv:2508.00824. Abstract We analyze the statistical problem of recovering an atomic signal, modeled as a discrete uniform distribution μ, from a binned Poisson convolution model. This question is motivated, among others, by super-resolution laser microscopy applications, where precise

Authors Nies TG, Staudt T, Munk A Journal The Annals of Applied Probability Citation Ann. Appl. Probab. 35(4): 2292-2362 (August 2025). Abstract Finding meaningful ways to measure the statistical dependency between random variables ξ and ζ is a timeless statistical endeavor. In recent years, several novel concepts, like the distance

Authors Staudt T, Hundrieser S, Munk A Journal SIAM Journal on Mathematics of Data Science Citation SIAM Journal on Mathematical Analysis 2025 57:2, 1452-1482. Abstract Kantorovich potentials denote the dual solutions of the renowned optimal transportation problem. Uniqueness of these solutions is relevant from both a theoretical and an algorithmic

Authors Hüselitz A, Li H, Munk A Journal Arxiv Citation arXiv:2503.05270. Abstract We consider the problem of sequential estimation of a single change point in a piecewise linear regression model under a Gaussian setup. We demonstrate that a certain CUSUM-type statistic attains the minimax optimal rates for localizing the change