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Mathematics

D-Index
42
Citations
6820
World Ranking
1802
National Ranking
107

Overview

Axel Munk is a researcher affiliated with the University of Göttingen in Germany. Their work primarily spans the fields of Mathematics and Biochemistry, Genetics and Molecular Biology. Munk's research subfields include Statistics and Probability, Biophysics, Applied Mathematics, Molecular Biology, and Artificial Intelligence.

The scientist's research topics cover a broad range of areas related to statistical methods, mathematical theory, and biological applications. Notable main research topics include Statistical Methods and Inference, Markov Chains and Monte Carlo Methods, Advanced Fluorescence Microscopy Techniques, Point Processes and Geometric Inequalities, Cell Image Analysis Techniques, Geometric Analysis and Curvature Flows, and Bayesian Methods and Mixture Models.

Munk has published extensively, with frequent publication venues including arXiv (Cornell University), SIAM Journal on Mathematics of Data Science, The Annals of Statistics, bioRxiv (Cold Spring Harbor Laboratory), and Nature Computational Science. This indicates a strong presence in both preprint and peer-reviewed journals spanning statistics, mathematics, and computational science.

Recent papers authored or co-authored by Munk demonstrate a focus on statistical methodology and applications in computational sciences:

  • Seeded binary segmentation: a general methodology for fast and optimal changepoint detection, 2022, Biometrika
  • Colocalization for super-resolution microscopy via optimal transport, 2021, Nature Computational Science
  • Testing for dependence on tree structures, 2020, Proceedings of the National Academy of Sciences
  • Empirical Regularized Optimal Transport: Statistical Theory and Applications, 2020, SIAM Journal on Mathematics of Data Science
  • Multiscale Quantile Segmentation, 2020, Journal of the American Statistical Association

The scientist has collaborated frequently with several colleagues. The most recurrent co-authors include Housen Li, Marcel Klatt, Shayan Hundrieser, Thomas Staudt, and Frank Werner. These collaborations reflect a network of researchers working at the intersection of mathematics, statistics, and computational biology.

Best Publications

  • Multiscale change point inference

    Klaus Frick;Axel Munk;Axel Munk;Hannes Sieling

  • Box-Type Approximations in Nonparametric Factorial Designs

    Edgar Brunner;Holger Dette;Axel Munk

  • Convergence rates of general regularization methods for statistical inverse problems and applications

    Nicolai Bissantz;T. Hohage;Axel Munk;F. Ruymgaart

  • INTRINSIC SHAPE ANALYSIS: GEODESIC PCA FOR RIEMANNIAN MANIFOLDS MODULO ISOMETRIC LIE GROUP ACTIONS

    Stephan Huckemann;Thomas Hotzand;Axel Munk;Georgia Augusta

  • Consistencies and rates of convergence of jump-penalized least squares estimators

    Leif Boysen;Angela Kempe;Volkmar Liebscher;Axel Munk

  • Estimating the variance in nonparametric regression—what is a reasonable choice?

    H. Dette;A. Munk;T. Wagner

  • Inference for empirical Wasserstein distances on finite spaces

    Max Sommerfeld;Axel Munk;Axel Munk

  • Intrinsic shape analysis: Geodesic principal component analysis for Riemannian manifolds modulo Lie group actions. Discussion paper with rejoinder.

    S. Huckemann;T. Hotz;A. Munk

  • Testing heteroscedasticity in nonparametric regression

    H. Dette;A. Munk

  • Nonparametric validation of similar distributions and assessment of goodness of fit

    Axel Munk;Claudia Czado

  • An unbiased test for the bioequivalence problem

    Lawrence D. Brown;J. T. Gene Hwang;Axel Munk

  • Hidden Markov models for circular and linear-circular time series

    Hajo Holzmann;Axel Munk;Max Suster;Walter Zucchini

  • Identifiability of Finite Mixtures of Elliptical Distributions

    Hajo Holzmann;Axel Munk;Tilmann Gneiting

  • Consistency and rates of convergence of nonlinear Tikhonov regularization with random noise

    Nicolai Bissantz;Thorsten Hohage;Axel Munk

  • Validation of linear regression models

    Holger Dette;Axel Munk

  • Nonparametric comparison of several regression functions: exact and asymptotic theory

    Axel Munk;Holger Dette

  • Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials

    S. Huckemann;T. Hotz;A. Munk

  • Contributions of empirical and quantile processes to the asymptotic theory of goodness-of-fit tests

    Eustasio del Barrio;Juan A. Cuesta-Albertos;Carlos Matrán;Sándor Csörgö

  • On difference‐based variance estimation in nonparametric regression when the covariate is high dimensional

    Axel Munk;Nicolai Bissantz;Thorsten Wagner;Gudrun Freitag

  • Non-parametric confidence bands in deconvolution density estimation

    Nicolai Bissantz;Lutz Dümbgen;Hajo Holzmann;Axel Munk

Frequent Co-Authors

Holger Dette
Holger Dette Ruhr University Bochum
Markus Haltmeier
Markus Haltmeier University of Innsbruck
Claudia Steinem
Claudia Steinem University of Göttingen
Thorsten Hohage
Thorsten Hohage University of Göttingen
Claudia Czado
Claudia Czado Technical University of Munich
Bert L. de Groot
Bert L. de Groot Max Planck Society
Martin Korte
Martin Korte Technische Universität Braunschweig
Joachim Weickert
Joachim Weickert Saarland University
Stefan W. Hell
Stefan W. Hell Max Planck Society

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