1. Data assimilation - the collaborativ research centre SFB1294
  2. Open Science
  3. Publications

Publications

  • Gugushvili, S., Mariucci, E. and Meulen, van der F. (2019). Decompounding discrete distributions: A non-parametric Bayesian approach. To appear in Scandinavian Journal of Statistics. arXiv: 1903.11142 

  • Mariucci, E., Ray, K. and Szabó, B. (2019). A Bayesian nonparametric approach to log-concave density estimation. To appear in Bernoulli. arXiv: 1703.09531

  • Roelly, S. and Zass, A. (2019). Marked Gibbs point processes with unbounded interaction: an existence result. arXiv: 1911.12800

  • Reich, S. and Weissmann, S. (2019). Fokker-Planck particle systems for Bayesian inference: Computational approaches.arXiv:1911.10832

  • Reich, S. and Weissmann, S. (2019). Fokker-Planck particle systems for Bayesian inference: Computational approaches.arXiv:1911.10832

  • de Wiljes, J., Pathiraja, S. and Reich, S. (2019). Ensemble transform algorithms for nonlinear smoothing problems. (in press at SIAM SISC) arXiv:1901.06300

  • Lange, T. and Stannat, W. (2019). On the continuous time limit of Ensemble Square Root Filters. arXiv 1910.12493

  • Blanchard, G. and Zadorozhnyi, O. (2019). Concentration of weakly dependent Banach-valued sums and applications to statistical learning methods. Bernoulli, 25(4B), 3421-3458. doi:10.3150/18-BEJ1095 (arXiv: 1712.01934)

  • de Wiljes, J. and Tong, X. T. (2019). Analysis of a localised nonlinear Ensemble Kalman Bucy Filter with complete and accurate observations. arXiv:1908.10580

  • Castillo, A. M., Shprits, Y. Y., Ganushkina, N., Drozdov, A., Aseev, N., Wang, D. and Dubyagin, S. (2019). Simulations of the inner magnetospheric energetic electrons using the IMPTAM-VERB coupled model. Journal of Atmospheric and Solar-Terrestrial Physics. doi: 10.1016/j.jastp.2019.05.014 

  • Malem-Shinitski, N., Seelig, S. A., Reich, S. and Engbert, R. (2019). Bayesian inference for an exploration-exploitation model of human gaze control. Conference extended abstract. (manuscript)

  • Nuesken, N. and Reich, S. (2019). Note on Interacting Langevin diffusions: Gradient structure and ensemble Kalman sampler by Garbuno-Inigo, Hoffmann, Li and Stuart. arXiv:1908.10890

  • Houdebert, P. (2019). Phase transition of the non-symmetric Continuum Potts model. arXiv: 1908.10066

  • Avanesov, V. (2019). How to gamble with non-stationary X-armed bandits and have no regrets. arXiv:1908.07636

  • Avanesov, V. (2019). Structural break analysis in high-dimensional covariance structure. arXiv: 1803.00508

  • Avanesov, V. (2019). Nonparametric Change Point Detection in Regression. arXiv:1903.02603

  • Götze, F., Naumov, A., Spokoiny, V. and Ulyanov, V. (2019). Gaussian comparison and anti-concentration inequalities for norms of Gaussian random elements, Bernoulli, in print. arXiv:1708.08663

  • Lefakis, L., Zadorozhnyi, O. and Blanchard, G. (2019). Efficient Regularized Piecewise-Linear Regression Trees. arXiv: 1907.00275​​​​​​​ 

  • Zadorozhnyi, O., Blanchard, G. and Carpentier, A. (2019). Restless dependent bandits with fading memory. arXiv: 1906.10454 

  • Pathiraja, S. and Reich, S. (2019). Discrete gradients for computational Bayesian inference. Journal of Computational Dynamics (in press) arXiv:1901.06300

  • Ty, A.J.A., Fang, Z., Gonzales, R.A., Rozdeba, P.J. and Abarbanel, H.D.I. (2019). Machine Learning of Time Series Using Time-delay Embedding and Precision Annealing. arXiv:1902.05062

  • Trukenbrod, H. A., Barthelmé, S., Wichmann, F. A. and Engbert, R. (2019). Spatial statistics for gaze patterns in scene viewing: Effects of repeated viewing, Journal of Vision, 19(6):5, 1-19. doi: 10.1167/19.6.5

  • Blanchard, G., Mathé, P. and Mücke, N. (2019). Lepskii Principle in Supervised Learning. arXiv: 1905.10764 

  • Somogyvári, M. and Reich, S. (2019). Convergence tests for transdimensional Markov chains in geoscience imaging, Math Geosci, 2019. doi: 10.1007/s11004-019-09811-x

  • Nüsken, N., Reich, S. and Rozdeba, P. J. (2019). State and parameter estimation from observed signal increments, Entropy, Vol. 21(5), 505. arXiv:1903.10717 ;  doi: 10.3390/e21050505

  • Lontsi, A. M., García-Jerez, A., Molina-Villegas, J. C., Sánchez-Sesma, F. J., Molkenthin, C., Ohrnberger, M., Krüger, F., Wang, R. and Fäh, D. (2019). A generalized theory for full microtremor horizontal-to-vertical [H/V(z, f)] spectral ratio interpretation in offshore and onshore environments, Geophysical Journal International, 218(2), 1276–1297. doi: 10.1093/gji/ggz223 arXiv: 1907.04606 

  • Achdou, J., Lam, J., Carpentier, A. and Blanchard, G. (2019). A minimax near-optimal algorithm for adaptive rejection sampling. Accepted for ALT'2019. Arxiv 1810.09390

  • Reich, S. (2019). Data assimilation: The Schrödinger perspective. Acta Numerica, Vol. 28, pp. 635-710. arXiv: 1807.08351

  • Leeuwen, P. J. v., Künsch, H.-R., Nerger, L., Potthast, R. and Reich, S. (2019). Particle filters for high-dimensional geoscience applications: a review. Quarterly J Royal Meteorlog. Soc., published online 22 April 2019. arXiv: 1807.10434v2

  • Wahl, M. (2019). A note on the prediction error of principal component regression.arXiv: 1811.02998

  • Katz-Samuels, J., Blanchard, G. and Scott, C. (2019). Decontamination of Mutual Contamination Models. To appear in Journal of Machine Learning Research. arXiv 1710.01167

  • Pasemann, G. and Stannat, W. (2019). Drift Estimation for Stochastic Reaction-Diffusion Systems. arXiv: 1904.04774

  • Carpentier, A., Duval, C. and Mariucci, E. (2019). Total variation distance for discretely observed Lévy processes: a Gaussian approximation of the small jumps. arXiv: 1810.02998

  • Reiß, M. and Wahl, M. (2019). Non-asymptotic upper bounds for the reconstruction error of PCA. Annals of Statistics, to appear. arXiv 1609.03779

  • Altmeyer, R. and Reiß, M. (2019). Nonparametric estimation for linear SPDEs from local measurements.arXiv: 1903.06984

  • Rastogi, A., Blanchard, G. and Mathé, P. (2019). Convergence analysis of Tikhonov regularization for non-linear statistical inverse learning problems.arXiv 1902.05404

  • Rothkegel, L. O., Schütt, H. H., Trukenbrod, H. A., Wichmann, F. A. and Engbert, R. (2019). Searchers adjust their eye-movement dynamics to target characteristics in natural scenes. Scientific Reports, 9, article no. 1635. doi: 10.1038/s41598-018-37548-w

  • Seelig, S. A., Rabe, M. M., Malem-Shinitski, N., Risse, S., Reich, S., and Engbert, R. (2019). Bayesian parameter estimation for the SWIFT model of eye-movement control during reading. arXiv: 1901.11110.

  • Schneider, S. and Heinecke, L. (2019). The need to transform Science Communication from being multi-cultural via cross-cultural to intercultural, Adv. Geosci., 46, 11-19, doi: 10.5194/adgeo-46-11-2019 

  • Duval, C. and Mariucci, E. (2019). Compound Poisson approximation to estimate the Lévy density. arXiv: 1702.08787 

  • Lange, T. and Stannat, W. (2019). On the continuous time limit of the Ensemble Kalman Filter. arXiv: 1901.05204v1

  • Blanchard, G., Neuvial, P. and Roquain, E. (2019). Post hoc inference via joint family-wise error rate control. (to appear in Annals of Statistics) arXiv: 1703.02307

  • Jirak, M. and Wahl, M. (2018). Perturbation bounds for eigenspaces under a relative gap condition.arXiv: 1803.03868

  • Donner, C. and Opper, M. (2018). Efficient Bayesian Inference of Sigmoidal Gaussian Cox Processes, Journal of Machine Learning Research 19, no 67, 1-34. Open Access

  • Morzfeld, M. and Reich, S. (2018). Data assimilation: mathematics for merging models and data. Snapshots of modern mathematics from Oberwolfach, 11. doi: 10.14760/SNAP-2018-011-EN

  • Makowski, S., Jäger, L., Abdelwahab, A., Landwehr, N. and Scheffer, T. (2018). A discriminative model for identifying readers and assessing text comprehension from eye movements. Proceedings of the European Conference on Machine Learning (ECML-2018). Open Access

  • Blanchard, G., Hoffmann, M. and Reiß, M. (2018). Early stopping for statistical inverse problems via truncated SVD estimation. Electron. J. Statist. 12 (2): 3204-3231. arXiv 1710.07278; doi: 10.1214/18-EJS1482

  • Blanchard, G. and Mücke, N. (2018). Parallelizing Spectral Algorithms for Kernel Learning. Journal of Machine Learning Research (30):1-29, 2018. Open Access

  • Alonso, S., Stange, M. and Beta, C. (2018). Modeling random crawling, membrane deformation and intracellular polarity of motile amoeboid cells. PLoS ONE, 13(8): e0201977. doi: 10.1371/journal.pone.0201977

  • Cherstvy, A. G., Nagel, O., Beta, C. and Metzler, R. (2018). Non-Gaussianity, population heterogeneity, and transient superdiffusion in the spreading dynamics of amoeboid cells. doi: 10.1039/c8cp04254c

  • Pathiraja, S. and van Leeuwen, P.J. (2018). Model uncertainty estimation in data assimilation for multi-scale systems with partially observed resolved variables, Quarterly Journal of the Royal Meteorological Society, under review, arXiv: 1807.09621

  • Blanchard, G., Hoffmann, M. and Reiß, M. (2018). Optimal adaptation for early stopping in statistical inverse problems. SIAM/ASA Journal on Uncertainty Quantification 6(3): 1043-1075. arXiv 1606.07702; doi:10.1137/17M1154096

  • Zöller, G. (2018). A Statistical Model for Earthquake Recurrence Based on the Assimilation of Paleoseismicity, Historic Seismicity, and Instrumental Seismicity. Journal of Geophysical Research: Solid Earth, 123, 4906-4921. doi: 10.1029/2017JB015099

  • Donner, C. and Opper, M. (2018). Efficient Bayesian Inference for a Gaussian Process Density Model, Proc. in Conference on Uncertainty in Artificial Intelligence, 2018. Open Access

  • Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L. and Moradkhani, H. (2018). Time varying parameter models for catchments with land use change: The importance of model structure, Hydrology and Earth System Sciences, 22(5), 2903-2919. doi: 10.5194/hess-22-2903-2018

  • Jirak, M. and Wahl, M. (2018). Relative perturbation bounds with applications to empirical covariance operators.arXiv: 1802.02869

  • Fiedler, B., Zöller, G., Holschneider, M. and Hainzl, S. (2018). Multiple Change‐Point Detection in Spatiotemporal Seismicity Data, Bulletin of the Seismological Society of America. 108 (3A): 1147-1159. doi: 10.1785/0120170236

  • Bachoc, F., Blanchard, G. and Neuvial, P. (2018). On the post selection inference constant under restricted isometry properties. Electron. J. Statist. 12(2): 3736-3757. doi: 10.1214/18-EJS1490

  • de Wiljes, J., Reich, S. and Stannat, W. (2018). Long-Time Stability and Accuracy of the Ensemble Kalman--Bucy Filter for Fully Observed Processes and Small Measurement Noise. SIAM Journal on Applied Dynamical Systems, 17, 1152-1181. arXiv: 1612.06065; doi: 10.1137/17M1119056

  • Moradkhani, H., Nearing, G., Abbaszadeh, P. and Pathiraja, S. (2018).  Fundamentals of Data Assimilation and Theoretical Advances. In: Duan, Q., Pappenberger, F., Thielen, J., Wood, A., Cloke, H.L. and Schaake, J. C. (eds), Handbook of Hydrometeorological Ensemble Forecasting. Berlin, Heidelberg: Springer Berlin Heidelberg, pp. 1 -26. doi: 10.1007/978-3-642-40457-3_30-1 

  • Silin, I. and Spokoiny, V. (2018). Bayesian inference for spectral projectors of the covariance matrix, Electron. J. Statist. 12(1), 1948-1987. doi:10.1214/18-EJS1451; arXiv:1711.11532

  • Pathiraja, S., Anghileri, D., Burlando, P., Sharma, A., Marshall, L. and Moradkhani, H. (2018). Insights on the impact of systematic model errors on data assimilation performance in changing catchments, Advances in Water Resources, 113 (December 2017), 202-222. doi: 10.1016/j.advwatres.2017.12.006

  • Blanchard, G., Carpentier, A. and Gutzeit, M. (2018). Minimax Euclidean Separation Rates for Testing Convex Hypotheses in Rd. Electron. J. Statist. 12 (2): 3713-3735. doi:10.1214/18-EJS1472

  • Pathiraja, S., Moradkhani, H., Marshall, L., Sharma, A. and Geenens, G. (2018). Data-Driven Model Uncertainty Estimation in Hydrologic Data Assimilation, Water Resources Research, 54(2), 1252-1280. doi: 10.1002/2018WR022627

  • Ni, B., Cao, X., Shprits, Y. Y., Summers, D., Gu, X., Fu, S. and Lou, Y. (2018). Hot Plasma Effects on the Cyclotron-Resonant Pitch-Angle Scattering Rates of Radiation Belt Electrons Due to EMIC Waves. Geophysical Research Letters, 45, 21-30. doi: 10.1002/2017GL07602

  • Gribonval, R., Blanchard, G.,  Keriven, N. and Traonmilin, Y. (2017). Compressive Statistical Learning with Random Feature Moments.arXiv 1706.07180

  • Blanchard, G., Deshmukh, A., Dogan, U., Lee, G. and Scott, C. (2017). Domain Generalization by Marginal Transfer Learning. arXiv 1711.07910

  • Zhelavskaya, I. S., Shprits, Y. Y. and Spasojevic, M. (2017). Empirical modeling of the plasmasphere dynamics using neural networks. Journal of Geophysical Research: Space Physics, 122, 11227–11244. doi:10.1002/2017JA024406

  • Aseev, N. A., Shprits, Y. Y., Drozdov, A. Y., Kellerman, A. C., Usanova, M. E., Wang, D. and Zhelavskaya, I. S. (2017). Signatures of Ultrarelativistic Electron Loss in the Heart of the Outer Radiation Belt Measured by Van Allen Probes. Journal of Geophysical Research, 122, 10102-10111. doi: 10.1002/2017JA024485

  • Borovsky, J. E. and Shprits, Y. Y. (2017). Is the Dst Index Sufficient to Define All Geospace Storms?. Journal of Geophysical Research: Space Physics, 122, 11543-11547. doi:10.1002/2017JA024679

  • Angwenyi, D., de Wiljes, J. and Reich, S. (2017). Interacting particle filters for simultaneous state and parameter estimation. arXiv:1709.09199

  • de Wiljes, J., Acevedo, W. and Reich, S. (2017). A second-order accurate ensemble transform particle filter. SIAM Journal on Scientific Computing, 39, A1834-A1850. arXiv: 1608.08179; doi: 10.1137/16M1095184

  • Taghvaei, A., de Wiljes, J., Mehta, P. G. and Reich, S. (2017). Kalman filter and its modern extensions for the continuous-time nonlinear filtering problem. ASME Journal of Dynamical Systems, Measurement, and Control, 140(3), 030904. arXiv: 1702.07241; doi: 10.1115/1.4037780

  • Schütt, H. H., Rothkegel, L. O. M., Trukenbrod, H. A., Reich, S., Wichmann, F. A. and Engbert, R. (2017). Likelihood-based parameter estimation and comparison of dynamical cognitive models. Psychological Review, 124, 505-524. doi:10.1037/rev0000068