- Lammers, L., Nye, T.M.W., Huckemann, S.F. (2024).
Statistics for Phylogenetic Trees in the Presence of Stickiness. arXiv:2407.03977. Submitted. - Hundrieser, S, Eltzner, B., Huckemann, S.F. (2024).
A Lower Bound for Estimating Fréchet Means. arXiv:2402.12290. Submitted. - Hundrieser, S., Eltzner, B., Huckemann, S.F. (2024).
Finite Sample Smeariness of Fréchet Means and Application to Climate Electron. J. Statist. arXiv:2005.02321., 18 (2), 3274-3309. - 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), e0279336. - 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 doi.org/10.1101/2021.08.06.455406 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, 72(2), 271--293. - 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., 109 (5), e12893. - 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. - Garba, M.K., Nye, M.W., Lueg, J., Huckemann, S.F. (2021).
Information metrics for phylogenetic trees via distributions of discrete and continuous characters. Geometric Science of Information: 5th International Conference , 701-709. - Lueg, J., Garba, M.K., Nye, M.W.,Huckemann, S.F. (2021).
Wald space for phylogenetic trees. Geometric Science of Information: 5th International Conference , 710-717. - 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 https://doi.org/10.1073/pnas.2023615118.. - 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), 39473963. - 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. - 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, arxiv.org 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., arXiv.org 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 arXiv.org 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.
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