Humboldt-Universität zu Berlin - Mathematisch-Naturwissenschaftliche Fakultät - Research Unit 1735

2019

• M. Mohr, L. Selk. Estimating change points in nonparametric time series regression models. arxiv:1909.07178 (2019)
• H. Dette, N. Dörnemann. Likelihood ratio tests for many groups in high dimensions. arXiv:1905.10354 (2019)
• A. Neumann, T. Dickhaus. Non-parametric Archimedean generator estimation with implications for multiple testing. arXiv:1903.11371 (2019)
• T. Dickhaus, N. Sirotko-Sibirskaya​​. Simultaneous statistical inference in dynamic factor models: Chi-square approximation and model-based bootstrap. Computational Statistics & Data Analysis. https://doi.org/10.1016/j.csda.2018.08.012 (2019)

• F. Göbel, C. Gehrke, O. Zadorozhnyi, G. Blanchard. G-WAV: a Graph Wavelet toolbox. (Submitted to JMLR MLOSS, 2019) Code available at: https://gitup.uni-potsdam.de/Graph-Wavelets/G-WAV.git including manual at: https://gitup.uni-potsdam.de/Graph-Wavelets/G-WAV/blob/master/doc/manual-v1.1.pdf

2018

• H. Dette, G. M. Pan, Q. Yang. Estimating a change point in a sequence of very high-dimensional covariance matrices. arxiv:1807.10797 (2018)
• G. Blanchard, N. Mücke. Parallelizing Spectral Algorithms for Kernel Learning. Journal of Machine Learning Research 19, 1-29 (2018).
• F. Bachoc, G. Blanchard, P. Neuvial. On the post selection inference constant under restricted isometry properties. Electron. J. Statist. 12(2), 3736-3757 (2018).
• H. Dette, W. Wu​​. Change point analysis in non-stationary processes - a mass excess approach. To appear in Annals of Statistics. arXiv:1801.09874 (2018)
• G. Blanchard, M. Hoffmann, M. Reiß​​. Optimal adaptation for early stopping in statistical inverse problems. SIAM/ASA J. Uncertain. Quantif. 6, 1043-1075 (2018)
• G. Blanchard, M. Hoffmann, M. Reiß​​.  Early stopping for statistical inverse problems via truncated SVD estimation. Electron. J. Stat. 12, 3204-3231 (2018) 
• G. Blanchard, N. Mücke. Optimal Rates For Regularization Of Statistical Inverse Learning Problems. Found. Comput. Math. 18, 971-1013 (2018).
• A. Neumann, T. Bodnar, D. Pfeifer, T. Dickhaus​​. Multivariate multiple test procedures based on nonparametric copula estimation. Biometrical Journal. https://doi.org/10.1002/bimj.201700205 (2018)
• H. Dette, D. Tomecki, M. Venker. Random Moment Problems under Constraints. arXiv:1806.04652 (2018)
• G. Blanchard, F. Göbel, U. von Luxburg. Construction of Tight Frames on Graphs and Application to Denoising. In Handbook of Big Data Analytics, Härdle, W., Lu, H-S. and Shen, X. editors, Chapter 20, pp. 503-522, Springer (2018).
• N. Neumeyer, L. Selk, C. Tillier. Semi-parametric transformation boundary regression models. arXiv:1804.05783 (2018)

2017

• V. Avanesov, N. Buzun. Change-point detection in high-dimensional covariance structure. arXiv:1610.03783 (2017).
• G. Blanchard, A. Deshmukh, U. Dogan, G. Lee, C. Scott. Domain Generalization by Marginal Transfer Learning. arXiv:1711.07910 (2017)
• J. Katz-Samuels, G. Blanchard, C. Scott. Decontamination of Mutual Contamination Models. To appear in Journal of Machine Learning Research. arXiv:1710.01167 (2017).
• G. Blanchard, P. Neuvial, E. Roquain. Post hoc inference via joint family-wise error rate control. arXiv:1703.02307 (2017).
• T. Bodnar, H. Dette, N. Parolya. Testing for Independence of Large Dimensional Vectors. To appear in Annals of Statistics arXiv:1708.03964 (2017).
• T. Bodnar, T. Dickhaus. On the Simes inequality in elliptical models. Annals of the Institute of Statistical Mathematics, 69(1), 215-230 (2017)
• H. Dette, D. Tomecki, M. Venker.  Universality in Random Moment Problems. arXiv:1709.02266 (2017).
• D. Ghoshdastidar, M. Gutzeit, A. Carpentier and U. von Luxburg. Two-sample hypothesis testing for inhomogeneous random graphs. arXiv:1707.00833v2 (2017)
• D. Ghoshdastidar, M. Gutzeit, A. Carpentier and U. von Luxburg. Two-sample tests for large random graphs using network statistics. Proceedings of COLT; arXiv:1705.06168 (2017)
• R. Gribonval, G. Blanchard, N. Keriven, Y. Traonmilin. Compressive Statistical Learning with Random Feature Moments. arXiv:1706.07180 (2017)
• N. Klodt, N. Neumeyer. Specification tests in semiparametric models. arXiv:1709.06855 (2017).
• A. Neumann, T. Bodnar, T. Dickhaus. Estimating the Proportion of True Null Hypotheses under Copula Dependency. Research report of Stockholm University (download) (2017).
• M. Reiß, L. Selk. Estimating nonparametric functionals efficiently under one-sided errors. Bernoulli 23(2), 1022-1055 (2017).
• M. Reiß, M. Wahl. Functional estimation and hypothesis testing in nonparametric boundary models. arXiv:1708.02854 (2017).
• D. Tomecki, H. Dette. Determinants of Random Block Hankel Matrices. arXiv:1706.08914 (2017).

​2016

• H. Dette, D. Tomecki. Hankel Determinants of Random Moment Sequences. Journal of Theoretical Probability, 1-26 (2016).
• N. Baldin, M. Reiß. Unbiased estimation of the volume of a convex body. Stochastic processes and Applications, 126(12), 3716-3732 (2016).
• M. Bibinger, M. Jirak and M. Reiß. Volatility estimation under one-sided errors with applications to limit order books. Ann. Appl. Probab. 26(5), 2754-2790 (2016).
• T. Bodnar, H. Dette, N. Parolya. Spectral analysis of the Moore-Penrose inverse of a large dimensional sample covariance matrix. Journal of Multivariate Analysis, 148, 160-172 (2016).
• T. Bodnar, M. Reiß. Exact and Asymptotic Tests on a Factor Model in Low and Large Dimensions with Applications. Journal of Multivariate Analysis, 150, 125-151 (2016).
• I. Dattner, M. Reiß and M. Trabs. Adaptive quantile estimation in deconvolution with unknown error distribution. Bernoulli, 22(1), 143-192 (2016).
• N. Neumeyer, H. Noh and I. Van Keilegom. Heteroscedastic semiparametric transformation models: estimation and testing for validity. Statistica Sinice, 26, 925-954 (2016).
• R. Nickl, M. Reiß, J. Söhl and M. Trabs. High-frequency Donsker theorems for Lévy measures. Probability Theory and Related Fields, 164(1), 61–108 (2016).

• B. Mieth, M. Kloft, J.A. Rodriguez, S. Sonnenburg, R. Vobruba, C. Morcillo-Suárez, X. Farré, U.M. Marigorta, E. Fehr, T. Dickhaus, G. Blanchard, D. Schunk, A. Navarro, K.-R. Müller. Combining Multiple Hypothesis Testing with Machine Learning Increases the Statistical Power of Genome-wide Association Studies. Scientific Reports 6: 36671, (2016).

• G. Blanchard, M. Flaska, G. Handy, S. Pozzi, C. Scott. Classification with asymmetric label noise: Consistency and maximal denoising. Electron. J. Statist., 10(2), 2780-2824 (2016).

• G. Blanchard, N. Krämer. Convergence rates of Kernel Conjugate Gradient for random design regression. Analysis and Applications 14 (6): 763-794 (2016).

2015

• A. Andresen. Finite sample behavior of a sieve profile estimator in the single index model. Electron. J. Statist., 9(2) :2528–2641 (2015). 

• T. Dickhaus, T. Royen. On multivariate chi-square distributions and their applications in testing multiple hypotheses. Statistics, 49 (2), 427-454. (2015).

• K. Jurczak. Weak convergencne of the empirical spectral distribution of ultra-high-dimensional banded sample covariance matrices. arXiv:1508.01101 (2015). 
• K. Jurczak. A universal expectation bound on empirical projections of deformed random matrices. Journal of Theoretical Probability, 28(2), 650-666 (2015).
• K. Jurczak, A. Rohde. Spectral Analysis of high-dimensional Sample Covariance Matrices with missing Observations. arXiv:1507.01615 (2015).
• S. Kurras. Symmetric Iterative Proportional Fitting. Proc. AISTATS 2015, JMLR W&CP 38:526–534 (2015).
• V. Spokoiny, M. Zhilova. Bootstrap confidence sets under a model misspecification. Ann. Statist., 43(6), 2653-2675 (2015)
• J. Stange, T. Bodnar, T. Dickhaus. Uncertainty quantification for the family-wise error rate in multivariate copula models. AStA Advances in Statistical Analysis, 99(3), 281-310 (2015).
• M. Trabs. Information bounds for inverse problems with application to deconvolution and Lévy models. Ann. Inst. H. Poincaré Probab. Statist., 51(4), 1620-1650 (2015).

2014

• M. Alamgir, U. von Luxburg and G. Lugosi. Density-preserving quantization with application to graph downsampling. Proc. COLT (2014).
• A. Andresen. A result on the bias of sieve profile estimators. arXiv:1406.4045 (2014).
• A. Andresen, V. Spokoiny. Critical dimension in profile semiparametric estimation. Electron. J. Statist. 8(2), 3077–3125 (2014).
• M. Bibinger, N. Hautsch, P. Malec and M. Reiß. Estimating the quadratic covariation matrix from noisy observations: local method of moments and efficiency. Annals of Statistics, 42(4), 1312–1346 (2014).
• G. Blanchard, S. Delattre and E. Roquain. Testing over a Continuum of Null Hypotheses with False Discovery Rate Control. Bernoulli, 20(1), 304-333 (2014).
• G. Blanchard, T. Dickhaus, E. Roquain and F. Villers. On least favorable configurations for step-up-down tests. Statistica Sinica 24 (1), 1–23 (2014).
• G. Blanchard, C. Scott. Decontamination of Mutually Contaminated Models. Proc. AISTATS 2014, JMLR W&CP 33:1-9 (2014).
• O. Bodnar, T. Bodnar and Y. Okhrin. Robust Surveillance of Covariance Matrices using a single Observation. Sankya A, 76(2), 219-256 (2014).
• T. Bodnar, T. Dickhaus. False Discovery Rate control under Archimedean copula. Electronic Journal of Statistics, 8 (2), 2207-2241 (2014).
• T. Bodnar, A. K. Gupta and N. Parolya. On the Strong Convergence of the Optimal Linear Shrinkage Estimator for Large Dimensional Covariance Matrix. Journal of Multivariate Analysis, 132, 215-228 (2014).
• T. Bodnar, N. Parolya and W. Schmid. Estimation of the Global Minimum Variance Portfolio in High Dimensions. arXiv:1406.0437 (2014).
• L. Cavalier, M. Reiß. Sparse model selection under heterogeneous noise: Exact penalisation and data-driven thresholding. Electr. J. Statist., 8, 432–455 (2014).
• K. Chaudhuri, S. Dasgupta, S. Kpotufex and U. von Luxburg. Consistent procedures for cluster tree estimation and pruning. IEEE Transactions of Information Theory, 60(12), (2014).
• T. Dickhaus. Simultaneous Statistical Inference with Applications in the Life Sciences. 
  Springer-Verlag Berlin Heidelberg, ISBN 978-3-642-45181-2 (2014).
• H. Drees, N. Neumeyer and L. Selk. Hypotheses tests in boundary regression models. 
  arXiv:1408.3979 (2014).
• A. K. Gupta, T. Bodnar. An exact test about the covariance matrix. Journal of Multivariate Analysis, 125, 176–189 (2014).
• K. Jurczak. A critical threshold level on Kendall’s tau statistic concerning minimax estimation of sparse correlation matrices. arXiv:1408.3525 (2014). 
• M. Jirak, A. Meister and M. Reiß. Adaptive estimation in nonparametric regression with one-sided errors. Annals of Statistics, 42(5), 1970-2002 (2014).
• S. Kurras, U. von Luxburg and G. Blanchard. The f-Ajusted Laplacian: a Diagonal Modification with a Geometric Interpretation. Proc. ICML 2014, JMLR W&CP 32:1530-1538 (2014).
• U. von Luxburg, A. Radl and M. Hein. Hitting and commute times in large random neighborhood graphs. Journal of Machine Learning Research, 15(1), 1751-1798 (2014).
• I. Tolstikhin, G. Blanchard and M. Kloft. Localized Complexities for Transductive Learning. Proc. COLT 2014, JMLR W&CP 35: 857-884 (2014).

2013

• M. Birke, N. Neumeyer. Testing monotonicity of regression functions – an empirical process approach. Scandinavian Journal of Statistics, 40, 438–454 (2013).
• T. Bodnar, A. K. Gupta. An exact test for a column of the covariance matrix based on a single observation. Metrika, 76(6) (2013).
• T. Bodnar, A. K. Gupta and N. Parolya. Optimal Linear Shrinkage Estimator for Large Dimensional Precision Matrix, arxiv:1308.0931 (2013).
• T. Dickhaus. Randomized p-values for multiple testing of composite null hypotheses. Journal of Statistical Planning and Inference, 143(11) (2013).
• T. Dickhaus, J. Gierl. Simultaneous test procedures in terms of p-value copulae.
  Proceedings on the 2nd Annual International Conference on Computational Mathematics, Computational Geometry & Statistics (CMCGS), 75-80 (2013).
• A. K. Gupta, T. Varga and T. Bodnar. Elliptically Contoured Models in Statistics and Portfolio Theory. Springer, New York, ISBN 978-1-4614-8153-9 (2013).
• U. von Luxburg, M. Alamgir. Density estimation from unweighted kNN graphs: a roadmap. In Proceedings Advances Neural Information Processing Systems (2013).
• A. Meister, M. Reiß. Asymptotic equivalence for nonparametric regression with non-regular errors. Probability Theory and Related Fields, 155(1), 201–229 (2013).
• C. Scott, G. Blanchard and G. Handy. Classification with Asymmetric Label Noise: Consistency and Maximal Denoising. Proc. Conf. on Learning Theory (COLT 2013), JMLR W&CP 30:489-511 (2013).
• L. Selk, N. Neumeyer. Testing for a change of the innovation distribution in nonparametric autoregression: the sequential empirical process approach. Scandinavian Journal of Statistics 40, 770–788 (2013).
• V. Spokoiny. Bernstein-von Mises Theorem for growing parameter dimension. arXiv:1302.3430 (2013).
• S. Volgushev, M. Birke, H. Dette and N. Neumeyer. Significance testing in quantile regression. Electronic Journal of Statistics 7, 105–145 (2013).

2012

• G. Blanchard, P. Mathé. Discrepancy Principle for Statistical Inverse Problems with Application to Conjugate Gradient Iteration. Inverse Problems, 28(11) (2012).
• T. Dickhaus, K. Straßburger, D. Schunk, C. Morcillo-Suarez, T. Illig and A. Navarro. How to analyze many contingency tables simultaneously in genetic association studies. Statistical Applications in Genetics and Molecular Biology,  11(4) (2012).
• M. Kloft, G. Blanchard. The Local Rademacher Complexity of lp-Norm Multiple Kernel Learning. Journal of Machine Learning Research, 13, 2465-2501 (2012).
• A. Rohde. Accuracy of empirical projections of high-dimensional Gaussian matrices. arXiv:1107.5481 (2012).
• V. Spokoiny. Parametric estimation. Finite sample theory. Ann. Statist. 40 (6), 2877–2909 (2012).