english version
Nächster Vortrag im Stochastischen Kolloquium:
13.04.2018, 14:15, Dr. Paul Joubert (IMS-Alumni) (Berlin)

"Working as a data scientist in industry" (Abstract).
Dr. Yoav Zemel, EPF Lausanne, spends SNF Early Postdoc Mobility fellowship at IMS: The IMS welcomes Dr. Yoav Zemel, who is spending with us an 18-months research visit from February 1st, 2018 to July 31st, 2019. Dr. Zemel is funded by a Swiss National Science Foundation Early Postdoc Mobility fellowship for the project Uncertainty Quantification for Optimal Transport mentored by Prof. Axel Munk (more information is available here).

Arbeitsgruppe "Angewandte und Mathematische Statistik"
Publikationen: Gesamtliste

Arbeitsgebiet: Statistical Inverse Problems

  • Werner, F. (2018).
    Adaptivity and oracle inequalities in linear statistical inverse problems: a (numerical) survey. New Trends in Parameter Identification for Mathematical Models, 291-316.
  • Proksch, K., Werner, F., Munk, A. (2018).
    Multiscale scanning in inverse problems. The Annals of Statistics, arXiv:1611.04537. To appear.
  • Li, Housen, Werner, Frank (2017).
    Empirical Risk Minimization as Parameter Choice Rule for General Linear Regularization Methods arXiv:1703.07809. Submitted.
  • 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: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. arXiv:1505.05272 IEEE Trans. Inf. Theory, 63, 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, 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, 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, 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, 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, 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).