RG Munk

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  • 2024 | Preprint
    ​ ​MultiMatch: Geometry-Informed Colocalization in Multi-Color Super-Resolution Microscopy​
    Naas, J.; Nies, G.; Li, H. ; Stoldt, S. ; Schmitzer, B. ; Jakobs, S.  & Munk, A. ​ (2024). DOI: https://doi.org/10.1101/2024.02.28.581557 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​Quick Adaptive Ternary Segmentation: An Efficient Decoding Procedure For Hidden Markov Models​
    Mösching, A.; Li, H.  & Munk, A. ​ (2023). DOI: https://doi.org/10.48550/arxiv.2305.18578 
    ​Details  DOI  arXiv 
  • 2023 | Preprint
    ​ ​Vimentin intermediate filaments structure and mechanically support microtubules in cells​
    Blob, A.; Ventzke, D.; Nies, G.; Dühmert, J. N.; Schmitzer, B. ; Munk, A.  & Schaedel, L. et al.​ (2023). DOI: https://doi.org/10.1101/2023.04.19.537509 
    ​Details  DOI 
  • 2023 | Journal Article | 
    ​ ​Towards Unbiased Fluorophore Counting in Superresolution Fluorescence Microscopy​
    Laitenberger, O.; Aspelmeier, T.; Staudt, T.; Geisler, C.; Munk, A.   & Egner, A. ​ (2023) 
    Nanomaterials, 13(3).​ DOI: https://doi.org/10.3390/nano13030459 
    ​Details  DOI 
  • 2023 | Journal Article
    ​ ​Statistical Analysis of Random Objects Via Metric Measure Laplacians​
    Mordant, G. & Munk, A.​ (2023) 
    SIAM Journal on Mathematics of Data Science, 5(2) pp. 528​-557​.​ DOI: https://doi.org/10.1137/22M1491022 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​Multiscale scanning with nuisance parameters​
    König, C.; Munk, A.& Werner, F.​ (2023). DOI: https://doi.org/10.48550/ARXIV.2307.13301 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​A scalable clustering algorithm to approximate graph cuts​
    Suchan, L.; Li, H.  & Munk, A. ​ (2023). DOI: https://doi.org/10.48550/ARXIV.2308.09613 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​Convergence of Empirical Optimal Transport in Unbounded Settings​
    Staudt, T.& Hundrieser, S.​ (2023). DOI: https://doi.org/10.48550/ARXIV.2306.11499 
    ​Details  DOI 
  • 2023 | Preprint
    ​ ​Empirical Optimal Transport under Estimated Costs: Distributional Limits and Statistical Applications​
    Hundrieser, S.; Mordant, G.; Weitkamp, C. A.& Munk, A. ​ (2023). DOI: https://doi.org/10.48550/ARXIV.2301.01287 
    ​Details  DOI 
  • 2023 | Journal Article
    ​ ​The Ultrametric Gromov–Wasserstein Distance​
    Mémoli, F.; Munk, A.; Wan, Z. & Weitkamp, C.​ (2023) 
    Discrete & Computational Geometry, 70(4) pp. 1378​-1450​.​ DOI: https://doi.org/10.1007/s00454-023-00583-0 
    ​Details  DOI 
  • 2023 | Journal Article
    ​ ​Minimax detection of localized signals in statistical inverse problems​
    Pohlmann, M.; Werner, F. & Munk, A.​ (2023) 
    Information and Inference, 12(3) art. iaad026​.​ DOI: https://doi.org/10.1093/imaiai/iaad026 
    ​Details  DOI 
  • 2022 | Preprint
    ​ ​A Unifying Approach to Distributional Limits for Empirical Optimal Transport​
    Hundrieser, S.; Klatt, M.; Staudt, T.& Munk, A. ​ (2022)
    ​Details  arXiv 
  • 2022 | Preprint
    ​ ​Empirical Optimal Transport between Different Measures Adapts to Lower Complexity​
    Hundrieser, S.; Staudt, T.& Munk, A. ​ (2022)
    ​Details  arXiv 
  • 2022 | Preprint
    ​ ​On the Uniqueness of Kantorovich Potentials​
    Staudt, T.; Hundrieser, S.& Munk, A. ​ (2022)
    ​Details  arXiv 
  • 2022 | Journal Article
    ​ ​Seeded binary segmentation: a general methodology for fast and optimal changepoint detection​
    Kovács, S.; Bühlmann, P.; Li, H. & Munk, A.​ (2022) 
    Biometrika, art. asac052​.​ DOI: https://doi.org/10.1093/biomet/asac052 
    ​Details  DOI 
  • 2022 | Preprint
    ​ ​Statistical analysis of random objects via metric measure Laplacians​
    Mordant, G.& Munk, A. ​ (2022). DOI: https://doi.org/10.48550/arXiv.2204.06493 
    ​Details  DOI 
  • 2022 | Journal Article | Research Paper
    ​ ​Statistical Methods for Minimax Estimation in Linear Models with Unknown Design Over Finite Alphabets​
    Behr, M. & Munk, A. ​ (2022) 
    SIAM Journal on Mathematics of Data Science, 4(2) pp. 490​-513​.​ DOI: https://doi.org/10.1137/21M1398860 
    ​Details  DOI 
  • 2022 | Journal Article | 
    ​ ​Kantorovich–Rubinstein Distance and Barycenter for Finitely Supported Measures: Foundations and Algorithms​
    Heinemann, F.; Klatt, M. & Munk, A.​ (2022) 
    Applied Mathematics & Optimization, 87(1).​ DOI: https://doi.org/10.1007/s00245-022-09911-x 
    ​Details  DOI 
  • 2022 | Book Chapter
    ​ ​The Statistics of Circular Optimal Transport​
    Hundrieser, S.; Klatt, M.& Munk, A. ​ (2022)
    In:​SenGupta, Ashis; Arnold, Barry C.​ (Eds.), Directional Statistics for Innovative Applications. A Bicentennial Tribute to Florence Nightingale pp. 57​-82. ​Singapore: ​Springer. DOI: https://doi.org/10.1007/978-981-19-1044-9_4 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​A Variational View on Statistical Multiscale Estimation​
    Haltmeier, M.; Li, H.   & Munk, A. ​ (2022) 
    Annual Review of Statistics and Its Application, 9(1) pp. 343​-372​.​ DOI: https://doi.org/10.1146/annurev-statistics-040120-030531 
    ​Details  DOI 
  • 2022 | Preprint
    ​ ​Unbalanced Kantorovich-Rubinstein distance and barycenter for finitely supported measures: A statistical perspective​
    Heinemann, F.; Klatt, M.& Munk, A. ​ (2022). DOI: https://doi.org/10.48550/ARXIV.2211.08858 
    ​Details  DOI 
  • 2022 | Journal Article
    ​ ​Randomized Wasserstein Barycenter Computation: Resampling with Statistical Guarantees​
    Heinemann, F.; Munk, A. & Zemel, Y.​ (2022) 
    SIAM Journal on Mathematics of Data Science, 4(1) pp. 229​-259​.​ DOI: https://doi.org/10.1137/20M1385263 
    ​Details  DOI 
  • 2021 | Preprint
    ​ ​Minimax detection of localized signals in statistical inverse problems​
    Pohlmann, M.; Werner, F.& Munk, A. ​ (2021)
    ​Details  arXiv 
  • 2021 | Preprint
    ​ ​Kantorovich-Rubinstein distance and barycenter for finitely supported measures: Foundations and Algorithms​
    Heinemann, F.; Klatt, M.& Munk, A. ​ (2021)
    ​Details  arXiv 
  • 2021 | Preprint
    ​ ​Transport Dependency: Optimal Transport Based Dependency Measures​
    Nies, T. G.; Staudt, T.& Munk, A. ​ (2021)
    ​Details  arXiv 
  • 2021 | Preprint
    ​ ​Limit Distributions and Sensitivity Analysis for Entropic Optimal Transport on Countable Spaces​
    Hundrieser, S.; Klatt, M.& Munk, A. ​ (2021)
    ​Details  arXiv 
  • 2021 | Preprint
    ​ ​The Statistics of Circular Optimal Transport​
    Hundrieser, S.; Klatt, M.& Munk, A. ​ (2021)
    ​Details  arXiv 
  • 2021 | Preprint
    ​ ​Analyzing cross-talk between superimposed signals: Vector norm dependent hidden Markov models and applications​
    Vanegas, L. J.; Eltzner, B.; Rudolf, D. ; Dura, M.; Lehnart, S. E.  & Munk, A. ​ (2021). DOI: https://doi.org/10.48550/arXiv.2103.06071 
    ​Details  DOI  arXiv 
  • 2021 | Preprint
    ​ ​The ultrametric Gromov-Wasserstein distance​
    Mémoli, F.; Munk, A. ; Wan, Z.& Weitkamp, C.​ (2021)
    ​Details  arXiv 
  • 2021 | Journal Article | Research Paper
    ​ ​Multiple haplotype reconstruction from allele frequency data​
    Pelizzola, M.; Behr, M.; Li, H. ; Munk, A.   & Futschik, A.​ (2021) 
    Nature computational science, 1(4) pp. 262​-271​.​ DOI: https://doi.org/10.1038/s43588-021-00056-5 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​What is resolution? A statistical minimax testing perspective on superresolution microscopy​
    Kulaitis, G.; Munk, A.   & Werner, F.​ (2021) 
    The Annals of Statistics, 49(4).​ DOI: https://doi.org/10.1214/20-AOS2037 
    ​Details  DOI 
  • 2021 | Journal Article | Research Paper
    ​ ​Colocalization for super-resolution microscopy via optimal transport​
    Tameling, C.; Stoldt, S. ; Stephan, T. ; Naas, J.; Jakobs, S.   & Munk, A. ​ (2021) 
    Nature computational science, 1(3) pp. 199​-211​.​ DOI: https://doi.org/10.1038/s43588-021-00050-x 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Frame-constrained total variation regularization for white noise regression​
    del Álamo, M.; Li, H.   & Munk, A. ​ (2021) 
    The Annals of Statistics, 49(3).​ DOI: https://doi.org/10.1214/20-AOS2001 
    ​Details  DOI  Preprint 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Analysis of patchclamp recordings: model-free multiscale methods and software​
    Pein, F. ; Eltzner, B.   & Munk, A. ​ (2021) 
    European Biophysics Journal, 50(2) pp. 187​-209​.​ DOI: https://doi.org/10.1007/s00249-021-01506-8 
    ​Details  DOI  PMID  PMC 
  • 2021 | Journal Article | Research Paper | 
    ​ ​Heterogeneous Idealization of Ion Channel Recordings – Open Channel Noise​
    Pein, F. ; Bartsch, A.; Steinem, C.   & Munk, A. ​ (2021) 
    IEEE Transactions on NanoBioscience, 20(1) pp. 57​-78​.​ DOI: https://doi.org/10.1109/TNB.2020.3031202 
    ​Details  DOI  PMID  PMC 
  • 2021 | Journal Article | Research Paper
    ​ ​Multiscale Quantile Segmentation​
    Jula Vanegas, L.; Behr, M.   & Munk, A. ​ (2021) 
    Journal of the American Statistical Association, pp. 1​-14​.​ DOI: https://doi.org/10.1080/01621459.2020.1859380 
    ​Details  DOI 
  • 2021 | Journal Article
    ​ ​Posterior analysis of n in the binomial (n,p) problem with both parameters unknown—with applications to quantitative nanoscopy​
    Schmidt-Hieber, J.; Schneider, L. F.; Staudt, T.; Krajina, A.; Aspelmeier, T. & Munk, A. ​ (2021) 
    The Annals of Statistics, 49(6).​ DOI: https://doi.org/10.1214/21-AOS2096 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Randomised Wasserstein Barycenter Computation: Resampling with Statistical Guarantees​
    Heinemann, F.; Munk, A.  & Zemel, Y.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Variational Multiscale Nonparametric Regression: Algorithms and Implementation​
    del Alamo, M.; Li, H. ; Munk, A.  & Werner, F.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries​
    Kovács, S.; Li, H. ; Haubner, L.; Munk, A.  & Bühlmann, P.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Heterogeneous Idealization of Ion Channel Recordings -- Open Channel Noise​
    Pein, F.; Bartsch, A.; Steinem, C.  & Munk, A. ​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Gromov-Wasserstein Distance based Object Matching: Asymptotic Inference​
    Weitkamp, C. A.; Proksch, K.; Tameling, C.& Munk, A. ​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​What is resolution? A statistical minimax testing perspective on super-resolution microscopy​
    Kulaitis, G.; Munk, A.  & Werner, F.​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Seeded Binary Segmentation: A general methodology for fast and optimal change point detection​
    Kovács, S.; Li, H. ; Bühlmann, P.& Munk, A. ​ (2020)
    ​Details  arXiv 
  • 2020 | Preprint
    ​ ​Testing for dependence on tree structures​
    Behr, M.; Ansari, M. A.; Munk, A.  & Holmes, C.​ (2020). DOI: https://doi.org/10.1101/622811 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​Multiple Haplotype Reconstruction from Allele Frequency Data​
    Pelizzola, M.; Behr, M.; Li, H. ; Munk, A.  & Futschik, A.​ (2020). DOI: https://doi.org/10.1101/2020.07.09.191924 
    ​Details  DOI 
  • 2020 | Preprint
    ​ ​An antibiotic-resistance conferring mutation in a neisserial porin: Structure, ion flux, and ampicillin binding​
    Bartsch, A.; Ives, C. M.; Kattner, C.; Pein, F.; Diehn, M.; Tanabe, M.& Munk, A.  et al.​ (2020). DOI: https://doi.org/10.1101/2020.11.06.369579 
    ​Details  DOI 
  • 2020 | Preprint | 
    ​ ​Analysis of Patchclamp Recordings: Model-Free Multiscale Methods and Software​
    Pein, F.; Eltzner, B.& Munk, A. ​ (2020)
    ​Details 
  • 2020 | Journal Article | Research Paper | 
    ​ ​Statistical Molecule Counting in Super-Resolution Fluorescence Microscopy: Towards Quantitative Nanoscopy​
    Staudt, T.; Aspelmeier, T. ; Laitenberger, O.; Geisler, C. ; Egner, A.   & Munk, A. ​ (2020) 
    Statistical Science, 35(1) pp. 92​-111​.​ DOI: https://doi.org/10.1214/19-STS753 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper | 
    ​ ​Testing for dependence on tree structures​
    Behr, M. ; Ansari, M. A.; Munk, A.   & Holmes, C.​ (2020) 
    Proceedings of the National Academy of Sciences of the United States of America, 117(18) pp. 9787​-9792​.​ DOI: https://doi.org/10.1073/pnas.1912957117 
    ​Details  DOI  PMID  PMC 
  • 2020 | Journal Article | Research Paper
    ​ ​Empirical Regularized Optimal Transport: Statistical Theory and Applications​
    Klatt, M.; Tameling, C. & Munk, A. ​ (2020) 
    SIAM Journal on Mathematics of Data Science, 2(2) pp. 419​-443​.​ DOI: https://doi.org/10.1137/19M1278788 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper
    ​ ​The essential histogram​
    Li, H. ; Munk, A. ; Sieling, H.   & Walther, G.​ (2020) 
    Biometrika, 107(2) pp. 347​-364​.​ DOI: https://doi.org/10.1093/biomet/asz081 
    ​Details  DOI 
  • 2020 | Journal Article | Research Paper | 
    ​ ​Variational Multiscale Nonparametric Regression: Algorithms and Implementation​
    del Alamo, M.; Li, H. ; Munk, A.   & Werner, F. ​ (2020) 
    Algorithms, 13(11) pp. 296​.​ DOI: https://doi.org/10.3390/a13110296 
    ​Details  DOI 
  • 2019 | Preprint
    ​ ​Multiscale quantile segmentation​
    Vanegas, L. J.; Behr, M.& Munk, A. ​ (2019)
    ​Details  arXiv 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Empirical optimal transport on countable metric spaces: Distributional limits and statistical applications​
    Tameling, C.; Sommerfeld, M. & Munk, A. ​ (2019) 
    The Annals of Applied Probability, 29(5) pp. 2744​-2781​.​ DOI: https://doi.org/10.1214/19-AAP1463 
    ​Details  DOI  Preprint 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Molecular contribution function in RESOLFT nanoscopy​
    Frahm, L.; Keller-Findeisen, J. ; Alt, P.; Schnorrenberg, S. ; del Álamo Ruiz, M.; Aspelmeier, T.   & Munk, A.  et al.​ (2019) 
    Optics Express, 27(15) pp. 21956​.​ DOI: https://doi.org/10.1364/OE.27.021956 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article | Research Paper | 
    ​ ​High-resolution experimental and computational electrophysiology reveals weak β-lactam binding events in the porin PorB​
    Bartsch, A.; Llabrés, S.; Pein, F.; Kattner, C.; Schön, M.; Diehn, M. & Tanabe, M. et al.​ (2019) 
    Scientific Reports, 9(1) art. 1264​.​ DOI: https://doi.org/10.1038/s41598-018-37066-9 
    ​Details  DOI  PMID  PMC 
  • 2019 | Journal Article | Research Paper | 
    ​ ​Multiscale Change-point Segmentation: Beyond Step Functions​
    Li, H. ; Guo, Q. & Munk, A. ​ (2019) 
    Electronic Journal of Statistics, 13(2) pp. 3254​-3296​.​ DOI: https://doi.org/10.1214/19-EJS1608 
    ​Details  DOI  Preprint 
  • 2018 | Preprint
    ​ ​Posterior analysis of $ in the binomial $(n,p)$ problem with both parameters unknown -- with applications to quantitative nanoscopy​
    Schmidt-Hieber, J.; Schneider, L. F.; Staudt, T.; Krajina, A.; Aspelmeier, T.& Munk, A. ​ (2018)
    ​Details  arXiv 
  • 2018 | Preprint
    ​ ​Frame-constrained Total Variation Regularization for White Noise Regression​
    del Álamo, M.; Li, H.  & Munk, A. ​ (2018)
    ​Details  arXiv 
  • 2017 | Preprint
    ​ ​Minimax estimation in linear models with unknown finite alphabet design​
    Behr, M.  & Munk, A. ​ (2017)
    ​Details  arXiv 
  • 2016 | Preprint
    ​ ​The Essential Histogram​
    Li, H. ; Munk, A. ; Sieling, H.  & Walther, G.​ (2016)
    ​Details  arXiv 

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