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News
Nächster Vortrag im Stochastischen Kolloquium:
06.12.2017, 11:15, Prof. Paul Fearnhead (Lancaster University)

"Detecting changes in slope with an Lo penalty" (Abstract).
Presseinformation: Dr. Vlada Limic, CNRS Straßburg, hat den Friedrich Wilhelm Bessel-Forschungspreis der Alexander von Humboldt-Stiftung erhalten. Sie forscht für ein Jahr am Institut für Mathematische Stochastik in der Arbeitsgruppe von Prof. Dr. Anja Sturm (Presseinformation).
Statistics Meets Friends: The workshop "Statistics Meets Friends - from biophysics to inverse problems and back -" takes place in Göttingen from November 29th to December 1st, 2017.
Publikationen

Arbeitsgruppe "Angewandte und Mathematische Statistik"
Publikationen: Gesamtliste

Arbeitsgebiet: Statistical Inverse Problems


  • Pein, F., Hotz, T., Tecuapetla-Gómez, I., Aspelmeier, T. (2017).
    clampSeg - an R package for idealisation of patch clamp recordings.
  • Pein, F., Tecuapetla-Gómez, I., Schütte, O. M., Steinem, C., Munk, A. (2017).
    Fully-Automatic Multiresolution Idealization for Filtered Ion Channel Recordings: Flickering Event Detection arxiv.org 1706.03671. Submitted.
  • Behr, M., Holmes, C., Munk, A. (2017).
    Multiscale blind source separation. The Annals of Statistics, arXiv:1608.07173. To appear.
  • Behr, M., Munk, A. (2017).
    Identifiability for blind source separation of multiple finite alphabet linear mixtures. IEEE Trans. Inf. Theory, 63(9), 5506 - 5517.
  • Hohage, T., Werner, F. (2016).
    Inverse Problems with Poisson Data: statistical regularization theory, applications and algorithms. Inverse Problems, 32, 093001 (56pp).
  • Hafi, N., Grunwald, M., van den Heuvel, L. S., Aspelmeier, T., Chen, J.-H., Zagrebelsky, M., Schuette, O. M., Steinem, C., Korte, M., Munk, A., Walla, J. P. (2016).
    Corrigendum: Fluorescence nanoscopy by polarization modulation and polarization angle narrowing. Nature Methods, 13, 101.
  • Hafi, N., Grunwald, M., van den Heuvel, l.S., Aspelmeier, T., Steinem, C., Korte, M., Munk, A., Walla, J.P. (2016).
    Reply to "Polarization modulation adds little additional information to super-resolution fluorescence microscopy" Nature Methods, 13, 8-9.
  • Hartmann, A., Huckemann, S., Dannemann, J., Laitenberger, O., Geisler, C., Egner, A., Munk, A. (2016).
    Drift estimation in sparse sequential dynamic imaging: with application to nanoscale fluorescence microscopy. J. Royal Statist. Society, Ser. B, arxiv.org 1403.1389, 78(3), 563–587.
  • Ta, H., Keller, J., Haltmeier, M. Saka, S.K., Schmied, J., Opazo, F., Tinnefeld, P., Munk, A., Hell, S.W. (2015).
    Mapping molecules in scanning far-field fluorescence nanoscopy. Nature Communications, 6, 1-7, DOI: 10.1038/ncomms8977.
  • Aspelmeier, T., Egner, A., Munk, A. (2015).
    Modern Statistical Challenges in High Resolution Fluorescence Microscopy. Annual Review of Statistics and its Application, 2, 163-202.
  • Sabel, T. (2014).
    Simultaneous Confidence Statements about the Diffusion Coefficient of an Itô -Process with Application to Spot Volatility Estimation. Dissertation, published via eDiss of SUB Göttingen.
  • Hafi, N., Grunwald, M., van den Heuvel, L.S., Aspelmeier, T., Chen, J.-H., Zagrebelsky, M., Schütte, O.M., Steinem, C., Korte, M., Munk, A., Walla, P.J. (2014).
    Fluorescence nanoscopy by polarization modulation and polarization angle narrowing. Nature Methods, 11, doi: 10.1038/nmeth.2919.
  • Sabel, T., Schmidt-Hieber, J. (2014).
    Matlab Toolbox Spotvol.
  • Greb, F., Krivobokova, T., Munk, A., von Cramon-Taubadel, S. (2014).
    Regularized Bayesian estimation of generalized threshold regression models. Bayesian Analysis, 9(1), 171-196 (Preprint).
  • Frick, S., Hohage, T., Munk, A. (2014).
    Asymptotic laws for change point estimation in inverse regression. Statistica Sinica, 24, 555-575 (Preprint).
  • Sabel, T., Schmidt-Hieber, J. (2014).
    Asymptotically efficient estimation of a scale parameter in Gaussian time series and closed-form expressions for the Fisher information (with Supplement). Bernoulli, 20(2), 747-774.
  • Haltmeier, M., Munk, A. (2014).
    Extreme value analysis of empirical frame coefficients and implications for denoising by soft-thresholding. Applied and Computational Harmonic Analysis, 36 (3), 434–460.
  • Yalunin, S. V., Herink, G., Solli, D. R., Krüger, M., Hommelhoff, P., Diehn, M., Munk, A., Ropers, C. (2013).
    Field localization and rescattering in tip-enhanced photoemission. Ann. Phys., 525, L12-L18.
  • Li, H., Haltmeier, M., Zhang, S., Frahm, J., Munk, A. (2013).
    Aggregated Motion Estimation for Image Reconstruction in Real-Time MRI (Supplements). Magnetic Resonance in Medicine, DOI: 10.1002/mrm.25020.
  • Hotz,T., Schütte, O., Sieling, H., Polupanow, T., Diederichsen, U., Steinem, C., Munk, A. (2013).
    Idealizing ion channel recordings by jump segmentation and statistical multiresolution analysis IEEE Trans. on NanoBioScience, 12, 376-386. (Preprint).
  • Aspelmeier, T., Ebel, G., Hoeschen, Ch. (2013).
    Tomographic imaging using Poissonian detector data. United States Patent, No. 8,559,690 B2.
  • Schmidt-Hieber, J., Munk, A., Duembgen, L. (2013).
    Multiscale Methods for Shape Constraints in Deconvolution: Confidence Statements for Qualitative Features. Annals of Statistics, 41, 1299-1328 (Preprint).
  • Aspelmeier, T., Ebel, G., Engeland, U. (2012).
    Radiation dose reductions for images contaminated with Poisson noise. doi: 10.1594/ecr2012/C-1181..
  • Krivobokova, T., Briones, R., Hub, J., Munk, A., de Groot, B. (2012).
    Partial least squares functional mode analysis: application to membrane proteins AQP1, Aqy1 and CLC-ec1 Biophysical Journal, 103, 786-796.
  • Frick, K., Marnitz, P. (2012).
    A Statistical Multiresolution Strategy for Image Reconstruction LNCS , 6667, 74-85.
  • Hotz, T., Marnitz, P., Stichtenoth, R., Davies, L., Kabluchko, Z., Munk, A. (2012).
    Locally adaptive image denoising by a statistical multiresolution criterion. Comp. Stat. Data Anal., 56(3), 543-558.
  • Frick, K., Marnitz, P., Munk, A. (2012).
    Statistical Multiresolution Dantzig Estimation in Imaging: Fundamental Concepts and Algorithmic Framework Electron. J. Stat., 6, 231-268.
  • Hoffmann, M., Munk, A., Schmidt-Hieber, J. (2012).
    Adaptive wavelet estimation of the diffusion coefficient under additive error measurements. Annales de l’Institute Henri Poincare, 48, 1186--1216 (Preprint).
  • Frick, K., Marnitz, P., Munk, A. (2012).
    Shape Constrained Regularisation by Statistical Multiresolution for Inverse Problems Inverse Problems, 28, 065006.
  • Hoeschen, Ch., Rafecas, M., Aspelmeier, T. (2011).
    Algorithms for image reconstruction. In Cantone, M.C., Hoeschen, Ch. (eds.), Radiation physics for nuclear medicine, Springer Verlag, Ch. 12.
  • Munk, A., Schmidt-Hieber, J. (2010).
    Lower bounds for volatility estimation in microstructure noise models. Borrowing Strength: Theory Powering Applications - A Festschrift for Lawrence D. Brown, IMS Collections, 6, 43-55 To appear (Preprint).
  • Huckemann, S., Kim, P., Koo, J.-Y., Munk, A. (2010).
    Moebius deconvolution on the hyperbolic plane with application to impedance density estimation. Ann. Statist., 38 (4), 2465-2498 (Preprint).
  • Munk, A., Schmidt-Hieber, J. (2010).
    Nonparametric estimation of the volatility function in a high-frequency model corrupted by noise. Electronic Journal of Statistics, 4, 781-821 (Preprint).
  • Munk, A., Schmidt-Hieber, J. (2009).
    The Estimation of Different Scales in Microstructure Noise Models from a Nonparametric Regression Perspective Oberwolfach Reports, 6, 2210-2212.
  • Bauer, F., Hohage, T., Munk, A. (2009).
    Iteratively regularized Gauss-Newton method for nonlinear inverse problems with random noise SIAM J. Num. Anal. , 47, 1827-1846 (Preprint).
  • Bissantz, N., Claeskens, G., Holzmann, H., Munk, A. (2009).
    Testing for lack of fit in inverse regression -- with applications to biophotonic imaging. J. Royal Statist. Soc. Ser. B, 71(1), 25-48 (Preprint).
  • Boysen, L., Bruns, S., Munk, A. (2009).
    Jump estimation in inverse regression. Elect.ronic J. Statist. , 3, 1322-1359 (Preprint).
  • Bissantz, N., Dümbgen, L., Munk, A., Stratmann, B. (2009).
    Convergence analysis of generalized iteratively reweighted least squares algorithms on convex function spaces.SIAM J. Optimization, 19, 1828-45.
  • Bissantz, N., Mair, B., Munk, A. (2008).
    A statistical stopping rule for MLEM reconstructions in PET. IEEE Nucl. Sci. Symp. Conf. Rec., 8, 4198-4200.
  • Bissantz, N., Hohage, T., Munk, A., Ruymgaart, F. (2007).
    Convergence rates of general regularization methods for statistical inverse problems and applications. SIAM J. Numerical Analysis, 45, 2610-2636. (Preprint).

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