Institut für Mathematische Stochastik

Publikationen: Prof. Dr. Huckemann

  • Hundrieser, S, Eltzner, B., Huckemann, S.F. (2024).
    A Lower Bound for Estimating Fréchet Means. arXiv:2402.12290. Submitted.
  • Lammers, L., Tran Van, D., Huckemann, S.F. (2023).
    Sticky Flavors. arXiv:2311.08846. Submitted.
  • Ulmer, S.,Van Tran, D., Huckemann, S.F. (2023).
    Exploring Uniform Finite Sample Stickiness. Geometric Science of Information 2023 proceedings, arXiv:2305.10324, I, 249--356.
  • Lammers, L.,Van Tran, D., Nye, TMW, Huckemann, S.F. (2023).
    Types of Stickiness in BHV Phylogenetic Tree Spaces and Their Degree. Geometric Science of Information 2023 proceedings, arXiv:2304.05025, I, 357--366.
  • Wiechers, H., Zobel M.,, Bennati, M., Tkach, I., Eltzner, B., Huckemann, S.F, Pokern, Y. (2023).
    Drift Models on Complex Projective Space for Electron-Nuclear Double Resonance. arXiv:2307.12414. Submitted.
  • Wiechers, H., Kehl, A., Hiller, M., Eltzner, B., Huckemannm S.F., Meyer, A., Tkach, I., Bennati, M., Pokern, Y. (2023).
    Bayesian optimization to estimate hyperfine couplings from 19F ENDOR spectra. Journal of Magnetic Resonance, 107491.
  • Hauke, L., Primeßnig, A., Eltzner, B., Radwitz, J., Huckemann, S.F., Rehfeld, F. (2023).
    FilamentSensor 2.0: An open-source modular toolbox for 2D/3D cytoskeletal filament tracking. PLOS One, 18(2).
  • Wiechers, H., Eltzner, B., Mardia, K. V., Huckemann, S. F. (2023).
    Learning torus PCA based classification for multiscale RNA backbone structure correction with application to SARS-CoV-2. bioRxiv Journal of the Royal Statistical Society, Series C, 72 (2), 271--293.
  • Hansen, P., Eltzner. B., Huckemann, S.F., Sommer, S. (2023).
    Diffusion Means in Geometric Spaces. arXiv:2105.12061 Bermoulli, 29(4),, 3141 - 3170.
  • Telschow, F.J.E, Pierrynowski, M., Huckemann, S.F. (2023).
    Confidence Tubes for Curves on SO(3) and Identification of Subject-Specific Gait Change after Kneeling. Journal of the Royal Statistical Society, Series C arXiv 1909.06583. To appear.
  • Huckemann, S., Li, XM., Pokern, Y., Sturm, A., (2022).
    Statistics of Stochastic Differential Equations on Manifolds and Stratified Spaces. Oberwolfach Reports, 18 (4), 2641 - 2663.
  • Lueg, J., Garba, M.K.,Nye, T.M.W., Huckemann, S.F. (2022).
    Foundations of the Wald Space for Phylogenetic Trees. Journal of the London Mathematical Society arXiv:2209.05332.. To appear.
  • Mardia, K. V., Wiechers, H., Eltzner, B., Huckemann, S. F. (2022).
    Principal component analysis and clustering on manifolds. Journal of Multivariate Analysis, 188, 104862 early online access.
  • Hiller, M., Tkach, I., Wiechers, H., Eltzner, B., Huckemann, S., Pokern, Y., Bennati, M. (2022).
    Distribution of Hß Hyperfine Couplings in a Tyrosyl Radical Revealed by 263 GHz ENDOR Spectroscopy. Applied Magnetic Resonance, 53 (7-9), 1015-1030.
  • Wieditz, J., Pokern, Y., Schuhmacher, D., Huckemann, S.F. (2022).
    Characteristic and Necessary Minutiae in Fingerprints. Journal of the Royal Statistical Society, Series C arXiv:2009.07910, 71, 27-50.
  • Huckemann, S.F. (2021).
    Comments on: Recent advances in directional statistics. TEST, 30 (1), 71--75.
  • Richter, R., Thai, D.H., Gottschlich, C., Huckemann, S.F. (2021).
    Filter Design for Image Decomposition and Applications to Forensics. Handbook of Mathematical Models and Algorithms in Computer Vision and Imaging: Mathematical Imaging and Vision (Eds.: Chen. K., Schönlieb, C.-B.,Tai, X.-C., Younes, L.), Springer, 1--28.
  • Pokern, Y., Eltzner, B., Huckemann, S. F., Beeken, C., Stubbe, J.A., Tkach, I., Bennati, M., Hiller, M. (2021).
    Statistical analysis of ENDOR spectra Proc. Natl. Acad. Science of the US, 118 (27), e2023615118
  • Richter, R., Thai, D.H., Huckemann, S.F. (2021).
    Generalized Intersection Algorithms with Fixpoints for Image Decomposition Learning SIAM Journal on Imaging Sciences. arXiv:2010.08661., 14 (3), 1273--1305.
  • Garba, M.K.,Nye, T.M.W., Lueg, J., Huckemann, S.F. (2021).
    Information geometry for phylogenetic trees Journal of Mathematical Biology arXiv:2003.13004., 82, article 19 online.
  • Eltzner, B., Galaz-Garcia, F., Huckemann, S.F., Tuschmann, W. (2021).
    Stability of the Cut Locus and a Central Limit Theorem for Fréchet Means of Riemannian Manifolds. Proceedings of the American Mathematical Society arXiv 1909.00410, 149 (9), 3947–3963.
  • Telschow, F.J.E, Huckemann, S.F. Pierrynowski, M. (2021).
    Functional Inference on Rotational Curves and Identification of Human Gait at the Knee Joint Scandinavian Journal of Statistics arXiv 1611.03665, 48, 1256–-1276.
  • Wiesner, S., Kaplan-Damary, N., Eltzner, B., and Huckemann, S. F. (2020).
    Shoe prints: The path from practice to science. Chapter XVII in Banks, D., Kafadar, K., and Kaye, D., editors, Handbook of Forensic Statistics, 391-410.
  • Huckemann, S.F., Eltzner, B. (2020).
    Data Analysis on Non-Standard Spaces WIREs Computational Statistics , 2021;13:e1526., early access.
  • Eltzner, B., Hauke, L., Huckemann, S., Rehfeldt, F., Wollnik, C. (2020).
    A Statistical and Biophysical Toolbox to Elucidate Structure and Formation of Stress Fibers, Chapter 10 in Nanoscale Photonic Imaging edited by T. Salditt, A. Egener and D.R. Luke, 263-282.
  • Hundrieser, S., Eltzner, B., Huckemann, S.F. (2020).
    Finite Sample Smeariness of Fréchet Means and Application to Climate arXiv:2005.02321.. Submitted.
  • Huckemann, S.F., Eltzner, B. (2020).
    Statistical Methods Generalizing Principal Component Analysis to Non-Euclidean Spaces Handbook of Variational Methods for Nonlinear Geometric Data, Chapter 10, 317-338.
  • Richter, R., Gottschlich, C., Mentch, L., Thai, D.H., Huckemann, S.F. (2019).
    Smudge Noise for Quality Estimation of Fingerprints and its Validation. IEEE Transactions on Information Forensics & Security, 14 (8), 1963--1974.
  • Markert, K., Krehl, K., Gottschlich, C., Huckemann, S. F. (2019).
    Detecting Anisotropy in Fingerprint Growth. Journal of the Royal Statistical Society, Series C arXiv 1801.06437, 68(4), 1007 –- 1027.
  • Eltzner, B., Huckemann, S. F. (2019).
    A Smeary Central Limit Theorem for Manifolds with Application to High Dimensional Spheres. Ann. Statist. arXiv 1801.06581, 47 (6), 3360-3381.
  • Düring,B., Gottschlich, C., Huckemann, S., Kreusser, L. M., Schönlieb, C.-B. (2019).
    An Anisotropic Interaction Model for Simulating Fingerprints. Journal of Mathematical Biology arXiv:1711.07417, 78 (7), 2171-2206.
  • Kim, B., Huckemann, S.F., Jung, S. (2019).
    Small sphere distributions for directional data with application to medical imaging. Scandinavian Journal of Statistics arXiv 1705.10013, 60, 651 -- 660.
  • Eltzner, B., Huckemann, S.F., Mardia, K.V. (2018).
    Torus principal component analysis with applications to RNA structure. Annals of Applied Statistics, 12(2), 1332 -- 1359.
  • Imdahl, C., Gottschlich, C., Huckemann, S.,Ohshika, K. (2018).
    Möbius moduli for fingerprint orientation fields Journal of Mathematical Imaging and Vision arXiv 1708.02158, 60, 651-660.
  • Huckemann, S.F., Eltzner, B. (2018).
    Backward nested descriptors asymptotics with inference on stem cell differentiation. Ann. Statist. arXiv 1609.00814, 46(5), 1994 -- 2019.
  • Huckemann, S. F., Eltzner, B. (2017).
    Essentials of backward nested descriptors inference. Functional Statistics and Related Fields, Chapter 18, 137--144.
  • Eltzner, B., Huckemann, S. (2017).
    Applying Backward Nested Subspace Inference to Tori and Polyspheres. Geometric Science of Information 2017 proceedings, 587--594.
  • Eltzner, B., Huckemann, S. (2017).
    Bootstrapping Descriptors for Non-Euclidean Data. Geometric Science of Information 2017 proceedings, 12--19.
  • Beneš, V., Večeřa, J., Eltzner, B., Wollnik, C., Rehfeldt, F., Králová, V., Huckemann, S.F. (2017).
    Estimation of parameters in a planar segment process with a biological application Image Analysis & Stereology , 36, 25-33.
  • Gottschlich, C., Tams, B., Huckemann, S. (2017).
    Perfect fingerprint orientation fields by locally adaptive global models. IET Biometrics, 6 (3), 183--190.
  • Huckemann, S.F., Hotz, T. (2016).
    Nonparametric Statistics on Manifolds and Beyond Chapter 18 in Rabi N. Bhattacharya Selected Papers, edited by Manfred Denker and Edward C. Waymire, 599-610.
  • Thai, D.H., Huckemann, S., Gottschlich, C. (2016).
    Filter Design and Performance Evaluation for Fingerprint Image Segmentation. PLoS ONE, 11(5), e0154160.
  • Huckemann, S.F., Kim. K.-R., Munk, A., Rehfeld, F., Sommerfeld, M., Weickert, J., Wollnik, C. (2016).
    The circular SiZer, inferred persistence of shape parameters and application to stem cell stress fibre structures. Bernoulli, 1404.3300, 22, 2113-2142.
  • 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. arXiv:1403.1389 Royal Statist. Society, Ser. , B78, 563–587.
  • Eltzner, B., Wollnik, C., Gottschlich, C., Huckemann, S., Rehfeldt, F. (2015).
    The Filament Sensor for Near Real-Time Detection of Cytoskeletal Fiber Structures PLoS ONE, 10 (5), e0126346.
  • Eltzner, B., Jung, S., Huckemann, S. (2015).
    Dimension Reduction on Polyspheres with Application to Skeletal Representations Geometric Science of Information 2015 proceedings, 22 - 29.
  • Eltzner, B., Huckemann, S.F., Mardia, K.V. (2015).
    Torus Principal Component Analysis with an Application to RNA Structures (old Version). arXiv:1511.04993 Submitted.
  • Imdahl, C., Huckemann, S., Gottschlich, C. (2015).
    Towards generating realistic synthetic fingerprint images Proc. Image and Signal Processing and Analysis (ISPA), 78-82.
  • Oehlmann, L., Huckemann, S., Gottschlich, C. (2015).
    Performance Evaluation of Fingerprint Orientation Field Reconstruction Methods. Proc. International Workshop on Biometrics and Forensics , 1-6.
  • Huckemann, S., Mattingly, J.C., Miller, E., Nolen, J. (2015).
    Sticky central limit theorems at isolated hyperbolic planar singularities Electronic Journal of Probability, 20, paper no. 78, 34 pp., 1410.6879 .
  • Schulz, J.,Jung, S., Huckemann, S., Pierrynowski, M., Marron, S., Pizer, S. (2015).
    Analysis of rotational deformations from directional data. Journal of Computational and Graphical Statistics, 24(2), 539 - 560 preprint.
  • Hotz, T., Huckemann, S. (2015).
    Intrinsic Means on the Circle: Uniqueness, Locus and Asymptotics. The Annals of the Institute of Statistical Mathematics, 67(1), 177-193 1108.2141 [stat.ME] [math.PR].
  • Huckemann, S.F. (2014).
    (Semi-)Intrinsic Statistical Analysis on Non-Euclidean Spaces. Chapter in Advances in Complex Data Modeling and Computational Methods in Statistics, Editors A. M. Paganoni and P. Secchi, 103-118.
  • Henke, M., Huckemann, S.F., Kurth, W., Sloboda, B. (2014).
    Reconstructing Leaf Growth Based on Non-destructive Digitizing and Low-Parametric Shape Evolution for Plant Modelling Over a Growth Cycle Silva Fennica, 48 (2), 1019..
  • Telschow, F.J.E., Huckemann, S.F., Pierrynowski, M. (2014).
    Asymptotics for Object Descriptors. Biometrical Journal, 56 (5), 781--785.
  • Skwerer, S., Bullitt, E., Huckemann, S., Miller, E., Oguz, I., Owen, M., Patrangenaru, V., Provan, S., Marron, J.S. (2014).
    Tree-oriented analysis of brain artery structure. Journal of Mathematical Imaging and Vision, 50, 126--143, DOI 10.1007/s10851-013-0473-0.
  • Huckemann, S. (2014).
    A Comment to Statistics on Manifolds and Landmark Based Image Analysis: A Nonparametric Theory with Applications Journal of Statistical Planning and Inference, 145, 33--36.
  • Huckeman, S., Hotz, T. (2014).
    On Means and Their Asymptotics: Circles and Shape Spaces Journal of Mathematical Imaging and Vision, 50(1), 98-106, DOI 10.1007/s10851-013-0462-3 (Preprint).
  • Gottschlich, C., Huckemann, S. (2014).
    Separating the Real From the Synthetic: Extended Minutiae Histograms as Fingerprints of Fingerprints. IET Biometrics, 3(4), 291-301.
  • Hotz, T., Huckemann, S., Le, H., Marron, J. S., Mattingly, J. C., Miller, E., Nolen, J., Owen, M., Patrangenaru, V., Skwerer, S. (2013).
    Sticky central limit theorems on open books. Annals of Applied Probability, 23(6) 2238-2258 , 1202.4267 [math.PR] [math.MG] [math.ST].
  • Pizer, S., Jung, S., Goswami, D., Zhao, X., Chaudhuri, R., Damon, J., Huckemann, S., Marron, S.J. (2013).
    Nested sphere statistics of skeletal models. Proc. Dagstuhl Workshop on Innovations for Shape Analysis: Models and Algorithms, Chapter 5, Preprint ..
  • Huckemann, S. (2012).
    A Comment to "A Microbiology Primer for Pyrosequencing" Quantitative Bio-Science, 31(2), 83-84.
  • Huckemann, S. (2012).
    On the Meaning of Mean Shape: Manifold Stability, Locus and the Two Sample Test Annals of the Institute of Statistical Mathematics, 64(6), 1227--1259.
  • Huckemann, S.F. (2011).
    Manifold stability and the central limit theorem for mean shape. Proceedings of the 30th Leeds Annual Statistical Research Workshop 5th-7th July, 2011, pdf.
  • Huckemann, S. (2011).
    Inference on 3D Procrustes Means: Tree Bole Growth, Rank Deficient Diffusion Tensors and Perturbation Models Scand. J. Statist., 38(3), 424--446 1001.0738 [stat.ME].
  • Huckemann, S. (2011).
    Intrinsic Inference on the Mean Geodesic of Planar Shapes and Tree Discrimination by Leaf Growth Ann. Statist., 39 (2), 1098–1124, arXiv 1009.3203 [stat.ME] (Preprint).
  • Huckemann, S., Hotz, T. (2010).
    Geodesic and parallel models for leaf shape Proceedings of the 29th Leeds Annual Statistical Research Workshop 6th-8th July 2010, pdf.
  • Huckemann, S., Hotz, T., Munk, A. (2010).
    Intrinsic shape analysis: Geodesic principal component analysis for Riemannian manifolds modulo Lie group actions. Discussion paper with rejoinder. Statistica Sinica, 20, 1-100 (Preprint).
  • Huckemann, S. (2010).
    Dynamic shape analysis and comparison of leaf growth. arXiv , 1002.0616v1 [stat.ME].
  • 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).
  • 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, 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, 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).
  • Huckemann, S., Hotz, T., Munk, A. (2010).
    Rejoinder on "Intrinsic shape analysis: Geodesic principal component analysis for Riemannian manifolds modulo Lie group actions." Statistica Sinica, 20, 1-100 (Preprint).
  • Huckemann, S., Hotz, T., Munk, A. (2009).
    Intrinsic two-way MANOVA for shape spaces. Proc. of the ISI2009, article.
  • Huckemann, S., Hotz, T. (2009).
    Principal Components Geodesics for Planar Shape. Journal of Multivariate Analysis, 100, 699-714 (Preprint).
  • Huckemann, S., Hotz, T., Munk, A. (2008).
    Global Models for the Orientation Field of Fingerprints: An Approach Based on Quadratic Differentials. IEEE Trans. Patt. Anal. Mach. Intell., 30(9), 1507-1519 (Preprint).
  • Huckemann, S. und Ziezold, H. (2006).
    Principal component analysis for Riemannian manifolds with an application to triangular shape spaces. Adv. Appl. Prob. (SGSA), 38, no. 2, 299 - 319.
  • Huckemann, S. (1988).
    Ein Extremalproblem für das harmonische Maß einer Familie von Extremalkontinua im Einheitskreis. Mitt. d. Math. Seminars Gießen, 184, 1 - 64 .
  • Huckemann, S. (1987).
    On the crossingpoint of Green's function of an annulus. Complex Variables Theory & Application, 8, no. 4, 281 - 291.
  • Huckemann, S. (1985).
    Spezielle Radialschlitzgebiete von festem Modul. Mitt. d. Math. Seminars Gießen, 169, 11 - 23.