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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

Forschergruppe FOR 916


  • Pein, F., Hotz, T., Sieling, H., Aspelmeier, T. (2017).
    stepR - an R package for change-point inference.
  • Grasmair, M., Li, H., Munk, A. (2017).
    Variational multiscale nonparametric regression: smooth functions Annales de l’Institute Henri Poincare (B), Probabilités et Statistiques, arxiv.org 1512.01068. To appear.
  • Pein, F., Sieling, H., Munk, A. (2017).
    Heterogeneuous change point inference (R package: HSMUCE). arxiv.org 1505.04898, Journ. Royal. Statist. Soc. Ser. B, 79, 1207–1227.
  • Enikeeva, F., Munk, A., Werner, F. (2017).
    Bump detection in heterogeneous Gaussian regression. Bernoulli 2018, 24, 2, 1266-1306.
  • Li, H., Munk, A., Sieling, H., Walther, G. (2016).
    The essential histogram arxiv.org 1612.07216. Submitted.
  • Li, H., Munk, A., Sieling, H. (2016).
    FDR-control in multiscale change-point segmentation. Electron. J. Statist., 10(1), 918-959.
  • Pein, F. (2015).
    HSMUCE - an R package for change-point inference for heterogeneous data.
  • Sabel, T., Schmidt-Hieber, J., Munk, A. (2015).
    Spot volatility estimation for high-frequency data: adaptive estimation in practice. Springer Lecture Notes in Statistics: Modeling and Stochastic Learning for Forecasting in High Dimension, 213-241.
  • 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.
  • Futschik, A., Hotz, T., Munk, A., Sieling, H. (2014).
    Multiscale DNA partitioning: statistical evidence for segments. Bioinformatics, doi: 10.1093/bioinformatics/btu180 (Preprint).
  • Sabel, T., Schmidt-Hieber, J. (2014).
    Matlab Toolbox Spotvol.
  • Frick, S., Hohage, T., Munk, A. (2014).
    Asymptotic laws for change point estimation in inverse regression. Statistica Sinica, 24, 555-575 (Preprint).
  • Frick, K., Munk, A., Sieling, H. (2014).
    Multiscale Change-Point Inference (software "stepR" for multiscale change point analysis "SMUCE") With discussion and rejoinder by the authors. Journ. Royal Statist. Society, Ser. B, , 76, 495-580. arXiv:1301.7212 long version with full proofs.
  • 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.
  • 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).
  • 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).
  • 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.
  • Serdyukova, N. (2012).
    Spatial adaptation in heteroscedastic regression: Propagation approach. Electron. J. Stat., 6, 861-907.
  • 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.
  • Krajina, A. (2012).
    A Method of Moments Estimator of Tail Dependence in Elliptical Copula Models. Journal of Statistical Planning and Inference, 142, 1811–1823 (Preprint).
  • 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.
  • Frick, K., Lorenz, D.A., Resmerita, E. (2011).
    Morozov´s principle for the augmented Lagrangian method applied to linear inverse problems Multiscale Model. Simul., 9, 1528-1548.
  • Munk, A. , Stockis, J.P., Valeinis, J., Giese, G. (2011).
    Neyman smooth goodness of fit tests for the marginal distribution of dependent data. Ann. Inst. Statist. Math. , 63, 639-659.
  • Frick, K., Scherzer, O. (2010).
    Regularization of ill-posed linear equations by the non-stationary Augmented Lagrangian Method J. Integral Equations Appl., 22, 217-258.
  • 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).
  • Hotz, T., Huckemann, S., Gaffrey, D., Munk, A., Sloboda, B. (2010).
    Shape spaces for pre-alingend star-shaped objects in studying the growth of plants. Journal of the Royal Statistical Society, Series C (Applied Statistics), 59 (1), 127-143 (Preprint).
  • Huckemann, S., Hotz, T., Munk, A. (2010).
    Intrinsic MANOVA for Riemannian Manifolds with an Application to Kendalls Spaces of Planar Shapes. IEEE Trans. Patt. Anal. Mach. Intell., 32 (4), 593-603, "Spotlight Paper" for this issue with its "Special Section on Shape Analysis and its Applications in Image Understanding", freely available until 18 March 2010 (Preprint).
  • Cai, T. T., Munk, A., Schmidt-Hieber, J. (2010).
    Sharp minimax estimation of the variance of Brownian motion corrupted with Gaussian noise. (Including Supplementary material). Statistica Sinica, 20, 1011-1024 (Preprint).
  • Balabdaoui, F., Mielke, M., Munk, A. (2009).
    The likelihood ratio test for non-standard hypotheses near the boundary of the null - with application to the assessment of non-inferiority. Statistics & Decisions, 27, 75-92.
  • 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).
  • Kabluchko, Z., Munk, A. (2009).
    Shao s theorem on the maximum of standardized random walk increments for multidimensional arrays. ESAIM Prob. Stat., 13, 409-416 (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.
  • Boysen, L., Kempe, A., Munk, A., Liebscher, V., Wittich, O. (2009).
    Consistencies and rates of convergence of jump penalized least squares estimators. Ann. Statist., 37, 157-183 (Preprint).
  • 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.
  • Dümbgen, L., Igl, B.W., Munk, A. (2008).
    P-values for classification. Electronic J. Stat., 2, 468-493 (Preprint).
  • Lakomek, N.A., Walter, K., Fares, C., Lange, O., de Groot, B., Grubmuller, H., Bruschweiler, R., Munk, A., Becker, S., Meiler, J., Griesinger, C. (2008).
    Self-consistent residual dipolar coupling based model-free analysis for the robust determination of nanosecond to microsecond protein dynamics. Journal of Biomolecular NMR, 41, 139-155, (supplement ) (Preprint).
  • 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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