Research Group Statistical Inverse Problems in Biophysics

Publications

Journal Article (60)

  1. 1.
    Sommerfeld, M.; Munk, A.: Inference for empirical Wasserstein distances on finite spaces. Journal of the Royal Statistical Society. Series B, Statistical Methodology 80 (1), pp. 219 - 238 (2018)
  2. 2.
    Behr, M.; Munk, A.: Identifiability for blind source separation of multiple finite alphabet linear mixtures. IEEE Transactions on Information Theory 63 (9), pp. 5506 - 5517 (2017)
  3. 3.
    Pein, F.; Sieling, H.; Munk, A.: Heterogeneous change point inference. Journal of the Royal Statistical Society. Series B, Statistical Methodology 79 (4), pp. 1207 - 1227 (2017)
  4. 4.
    Chen, Y. L.; Habeck, M.: Data-driven coarse graining of large biomolecular structures. PLoS One (2017)
  5. 5.
    Michalik, M.; Orwick-Rydmark, M.; Habeck, M.; Alva, V.; Arnold, T.; Linke, D.: An evolutionarily conserved glycine-tyrosine motif forms a folding core in outer membrane proteins. PLoS One (2017)
  6. 6.
    Tecuapetla-Gomez, I.; Munk, A.: Autocovariance estimation in regression with a discontinuous signal and m-dependent errors: A difference-based approach. Scandinavian Journal of Statistics 44 (2), pp. 346 - 368 (2017)
  7. 7.
    Mütze, T.; Konietschke, F.; Munk, A.; Friede, T.: A studentized permutation test for three-arm trials in the 'gold standard' design. Statistics in Medicine 36 (6), pp. 883 - 898 (2017)
  8. 8.
    Bauer, U.; Munk, A.; Sieling, H.; Wardetzky, M.: Persistence barcodes versus Kolmogorov signatures: Detecting modes of one-dimensional signals. Foundations of Computational Mathematics 17 (1), pp. 1 - 33 (2017)
  9. 9.
    Carstens, S.; Nilges, M.; Habeck, M.: Inferential structure determination of chromosomes from single-cell Hi-C data. PLoS Computational Biology (2016)
  10. 10.
    Huckemann, S.; Kim, K. R.; Munk, A.; Rehfeld, F.; Sommerfeld , M.; Weickert, J.; Wollnik, C.: The circular SiZer, inferred persistence of shape parameters and application to early stem cell differentiation. Bernoulli 22 (4), pp. 2113 - 2142 (2016)
  11. 11.
    Rippl, T.; Munk, A.; Sturm, A.: Limit laws of the empirical Wasserstein distance: Gaussian distributions. Journal of Multivariate Analysis 151, pp. 90 - 109 (2016)
  12. 12.
    Hohage, T.; Werner, F.: Inverse problems with Poisson data: Statistical regularization theory, applications and algorithms. Inverse Problems 32 (9), 093001 (2016)
  13. 13.
    Aspelmeier, T.; Wang, W.; Moore, M. A.; Katzgraber, H. G.: Interface free-energy exponent in the one-dimensional Ising spin glass with long-range interactions in both the droplet and broken replica symmetry regions. Physical Review E 94 (2), 022116 (2016)
  14. 14.
    Kühn, J.; Wong, L. E.; Pirkuliyeva, S.; Schulz, K.; Schwiegk, C.; Fünfgeld, K.; Keppler, S.; Batista, F. D.; Urlaub, H.; Habeck, M. et al.; Becker, S.; Griesinger, C.; Wienands, J.: The adaptor protein CIN85 assembles intracellular signaling clusters for B cell activation. Science Signaling (2016)
  15. 15.
    Hartmann, A.; Huckemann, S.; Dannemann, J.; Laitenberger, O.; Geisler, C.; Egner, A.; Munk, A.: Drift estimation in sparse sequential dynamic imaging, with application to nanoscale fluorescence microscopy. Journal of the Royal Statistical Society: Series B (Statistical Methodology) 78 (3), pp. 563 - 587 (2016)
  16. 16.
    Martini, J. W. R.; Diambra, L.; Habeck, M.: Cooperative binding: A multiple personality. Journal of Mathematical Biology 72 (7), pp. 1747 - 1774 (2016)
  17. 17.
    Singer, M.; Krivobokova, T.; Munk, A.; De Groot, B. L.: Partial least squares for dependent data. Biometrika 103 (2), pp. 351 - 362 (2016)
  18. 18.
    Aspelmeier, T.; Katzgraber, H. G.; Larson, D.; Moore, M. A.; Wittmann, M.; Yeo, J.: Finite-size critical scaling in Ising spin glasses in the mean-field regime. Physical Review E 93, 032123 (2016)
  19. 19.
    Mütze, T.; Munk, A.; Friede, T.: Design and analysis of three-arm trials with negative binomially distributed endpoints. Statistics in Medicine 35 (4), pp. 505 - 521 (2016)
  20. 20.
    Hafi, N.; Grunwald, M.; van den Heuvel, L. S.; Aspelmeier, T.; Steinem, C.; Korte, M.; Munk, A.; Walla, P. J.: Reply to "Polarization modulation adds little additional information to super-resolution fluorescence microscopy". Nature Methods 13 (1), pp. 8 - 9 (2016)
  21. 21.
    König, C.; Werner, F.; Hohage, T.: Convergence rates for exponentially ill-posed inverse problems with impulsive noise. SIAM Journal on Numerical Analysis 54 (1), pp. 341 - 360 (2016)
  22. 22.
    Li, H.; Munk, A.; Sieling, H.: FDR-control in multiscale change-point segmentation. Electronic Journal of Statistics (2016)
  23. 23.
    Knuth, K. H.; Habeck, M.; Malakar, N. K.; Mubeen, A. M.; Placek, B.: Bayesian evidence and model selection. Digital Signal Processing 47, pp. 50 - 67 (2015)
  24. 24.
    Shahid, S. A.; Nagaraj, M.; Chauhan, N.; Franks, T. W.; Bardiaux, B.; Habeck, M.; Orwick-Rydmark, M.; Linke, D.; van Rossum, B. J.: Festkörper-NMR-Studien an der Membrananker-Domäne von YadA in der bakteriellen Außenmembran. Angewandte Chemie 127 (43), pp. 12792 - 12797 (2015)
  25. 25.
    Shahid, S. A.; Nagaraj, M.; Chauhan, N.; Franks, T. W.; Bardiaux, B.; Habeck, M.; Orwick-Rydmark, M.; Linke, D.; van Rossum, B. J.: Solid-state NMR study of the YadA membrane-anchor domain in the bacterial outer membrane. Angewandte Chemie-International Edition 54 (43), pp. 12602 - 12606 (2015)
  26. 26.
    Habenstein, B.; Loquet, A.; Hwan, S.; Giller, K.; Vasa, S. K.; Becker, S.; Habeck, M.; Lange, A.: Hybrid structure of the type 1 pilus of uropathogenic Escherichia coli. Angewandte Chemie International Edition 54 (40), pp. 11691 - 11695 (2015)
  27. 27.
    Ta, H.; Keller, J.; Haltmeier, M.; Saka, S. K.; Schmied, J.; Opazo, F.; Tinnefeld, P.; Munk, A.; Hell, S. W.: Mapping molecules in scanning far-field fluorescence nanoscopy. Nature Communications (2015)
  28. 28.
    Munk, A.; Werner, F.: Discussion of “Hypotheses testing by convex optimization”. Electronic Journal of Statistics (2015)
  29. 29.
    Martini, J. W. R.; Habeck, M.: Comparison of the kinetics of different Markov models for ligand binding under varying conditions. Journal of Chemical Physics 142 (9), 094104 (2015)
  30. 30.
    Joubert, P.; Habeck, M.: Bayesian inference of initial models in cryo-electron microscopy using pseudo-atoms. Biophysical Journal 108 (5), pp. 1165 - 1175 (2015)
  31. 31.
    Werner, F.: On convergence rates for iteratively regularized Newton-type methods under a Lipschitz-type nonlinearity condition. Journal of Inverse and III-posed Problems 23 (1), pp. 75 - 84 (2015)
  32. 32.
    Habeck, M.: Nested sampling with demons. AIP Conference Proceedings 1641, pp. 121 - 129 (2015)
  33. 33.
    Martini, J. W. R.; Habeck, M.: Kinetics or equilibrium? – A commentary on a recent simulation study of semiochemical dose-response curves of insect olfactory sensing. Journal of Chemical Ecology 40 (11-12), pp. 1163 - 1164 (2014)
  34. 34.
    Li, H.; Haltmeier, M.; Zhang, S.; Frahm, J.; Munk, A.: Aggregated motion estimation for image reconstruction in real-time MRI. Magnetic Resonance in Medicine 72 (4), pp. 1039 - 1048 (2014)
  35. 35.
    Futschik, A.; Hotz, T.; Munk, A.; Sieling, H.: Multiscale DNA partitioning: Statistical evidence for segments. Bioinformatics 30 (16), pp. 2255 - 2262 (2014)
  36. 36.
    Frick, K.; Munk, A.; Sieling, H.: Multiscale change point inference. Journal of the Royal Statistical Society: Series B, Statistical Methodology 76 (3), pp. 495 - 580 (2014)
  37. 37.
    Hohage, T.; Werner, F.: Convergence rates for inverse problems with impulsive noise. SIAM Journal on Numerical Analysis (2014)
  38. 38.
    Hafi, N.; Grunwald, M.; van den Heuvel, L. S.; Aspelmeier, T.; Chen, J.; Zagrebelsky, M.; Schütte, O. M.; Steinem, C.; Korte, M.; Munk, A. et al.; Walla, P. J.: Fluorescence nanoscopy by polarization modulation and polarization angle narrowing. Nature Methods 11 (5), pp. 579 - 584 (2014)
  39. 39.
    Haltmeier, M.; Munk, A.: Extreme value analysis of frame coefficients and implications for image denoising. Applied and Computational Harmonic Analysis 36 (3), pp. 434 - 460 (2014)
  40. 40.
    Greb, F.; Krivobokova, T.; Munk, A.; von Cramon-Taubadel, S.: Regularized Bayesian estimation of generalized threshold regression models. Bayesian Analysis (2014)
  41. 41.
    Hotz, T.; Schuette, O. M.; Sieling, H.; Polupanow, T.; Diederichsen, U.; Steinem, C.; Munk, A.: Idealizing ion channel recordings by a jump segmentation multiresolution filter. IEEE Transactions on Nanobioscience 12 (4), pp. 376 - 386 (2013)
  42. 42.
    Frick, K.; Marnitz, P.; Munk, A.: Statistical multiresolution estimation for variational imaging: With an application in poisson-biophotonics. Journal of Mathematical Imaging and Vision 46 (3), pp. 370 - 387 (2013)
  43. 43.
    Greb, F.; von Cramon-Taubadel, S.; Krivobokova, T.; Munk, A.: The estimation of threshold models in price transmission analysis. American Journal of Agricultural Economics 95 (4), pp. 900 - 916 (2013)
  44. 44.
    Yalunin, S. V.; Herink, G.; Solli, D. R.; Krüger, M.; Hommelhoff, P.; Diehn, M.; Munk, A.; Ropers, C.: Field localization and rescattering in tip-enhanced photoemission. Annalen der Physik 525 (1-2), pp. L12 - L18 (2013)
  45. 45.
    Schmidt-Hieber, J.; Munk, A.; Duembgen, L.: Multiscale methods for shape constraints in deconvolution: Confidence statements for qualitative features. The Annlas of Statistics 41 (3), pp. 1299 - 1328 (2013)
  46. 46.
    Krivobokova, T.; Briones, R.; Hub, J. S.; Munk, A.; de Groot, B. L.: Partial least squares functional mode analysis: Application to the membrane proteins AQP1, Aqy1 and CLC-ec1. Biophysical Journal 103 (4), pp. 786 - 796 (2012)
  47. 47.
    Frick, K.; Marnitz, P.; Munk, A.: Shape constrained regularisation by statistical multiresolution for inverse problems: asymptotic analysis. Inverse Problems 28 (6), 065006 (2012)
  48. 48.
    Geisler, C.; Hotz, T.; Schönle, A.; Hell, S. W.; Munk, A.; Egner, A.: Drift estimation for single marker switching based imaging schemes. Optics Express 20 (7), pp. 7274 - 7289 (2012)
  49. 49.
    Hotz, T.; Marnitz, P.; Stichtenoth, R.; Davies, L.; Kabluchko, Z.; Munk, A.: Locally adaptive image denoising by a statistical multiresolution criterion. Computational Statistics and Data Analysis 56 (3), pp. 543 - 558 (2012)
  50. 50.
    Frick, K.; Marnitz, P.; Munk, A.: Statistical multiresolution Dantzig estimation in imaging: Fundamental concepts and algorithmic framework. Electronic journal of statistics (2012)
  51. 51.
    Hoffmann, M.; Munk, A.; Schmidt-Hieber, J.: Adaptive wavelet estimation of the diffusion coefficient under additive error measurements. Annales de l'Institute Henri Poincaré probabilités et statistiques (2012)
  52. 52.
    Grasmair, M.; Haltmeier, M.; Scherzer, O.: The residual method for regularizing ill-posed problems. Applied Mathematics and Computation 218 (6), pp. 2693 - 2710 (2011)
  53. 53.
    Gottschlich, C.; Hotz, T.; Lorenz, R.; Bernhardt, S.; Hantschel, M.; Munk, A.: Modeling the growth of fingerprints improves matching for adolescents. IEEE Transactions on Information Forensics and Security 6 (3), pp. 1165 - 1169 (2011)
  54. 54.
    Aspelmeier, T.; Zippelius, A.: The integrated density of states of the random graph Laplacian. Journal of Statistical Physics 144, pp. 759 - 773 (2011)
  55. 55.
    Haltmeier, M.: A mollification approach for inverting the spherical mean Radon transform. SIAM Journal on Applied Mathematics 71 (5), pp. 1637 - 1652 (2011)
  56. 56.
    Haltmeier, M.: Inversion formulas for a cylindrical Radon transform. SIAM Journal on Imaging Sciences 4 (3), pp. 789 - 806 (2011)
  57. 57.
    Grasmaier, M.; Haltmeier, M.; Scherzer, O.: Necessary and sufficient conditions for linear convergence of ℓ1-regularization. Communications on Pure and Applied Mathematics 64 (2), pp. 161 - 182 (2010)
  58. 58.
    Gottschlich, C.; Hotz, T.; Lorenz, R.; Bernhardt, S.; Hantschel, M.; Munk, A.: Modeling the growth of fingerprints improves matching for adolescents. IEEE Transactions on Information Forensics and Security 6 (3), pp. 1165 - 1169 (2010)
  59. 59.
    Huckemann, S.; Hotz, T.; Munk, A.: Intrinsic shape analysis: Geodesic principal component analysis for Riemannian manifolds modulo Lie group actions. Discussion paper with rejoinder. Statistica Sinica 20, pp. 1 - 100 (2010)
  60. 60.
    Haltmeier, M.; Leitao, A.; Resmerita, E.: On regularization methods of EM-Kaczmarz type. Inverse Problems 25 (7), 075008, pp. 075008-1 - 075008-17 (2009)

Book Chapter (2)

  1. 61.
    Munk, A.; Pricop, M.: On the self-regularization property of the EM algorithm for Poisson inverse problems. In: Statistical modelling and regression structures, pp. 431 - 446. Springer, Berlin (2010)
  2. 62.
    Grasmaier, M.; Haltmeier, M.; Scherzer, O.: Sparsity in inverse geophysical problems. In: Handbook of Geomathematics. Vol. 2, pp. 763 - 784. Springer, Berlin (2010)
 
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