| Bayesian linear regression with sparse priors I Castillo, J Schmidt-Hieber, A Van der Vaart The Annals of Statistics 43 (5), 1986-2018, 2015 | 222 | 2015 |
| Nonparametric regression using deep neural networks with ReLU activation function J Schmidt-Hieber Annals of Statistics 48 (4), 1875-1897, 2020 | 112 | 2020 |
| On adaptive posterior concentration rates M Hoffmann, J Rousseau, J Schmidt-Hieber The Annals of Statistics 43 (5), 2259-2295, 2015 | 61 | 2015 |
| Multiscale methods for shape constraints in deconvolution: confidence statements for qualitative features J Schmidt-Hieber, A Munk, L Dümbgen The Annals of Statistics 41 (3), 1299-1328, 2013 | 45 | 2013 |
| A comparison of deep networks with ReLU activation function and linear spline-type methods K Eckle, J Schmidt-Hieber Neural Networks 110, 232-242, 2019 | 43 | 2019 |
| Conditions for posterior contraction in the sparse normal means problem SL van der Pas, JB Salomond, J Schmidt-Hieber Electronic journal of statistics 10 (1), 976-1000, 2016 | 42 | 2016 |
| Nonparametric estimation of the volatility function in a high-frequency model corrupted by noise A Munk, J Schmidt-Hieber Electronic Journal of Statistics 4, 781-821, 2010 | 28 | 2010 |
| Lower bounds for volatility estimation in microstructure noise models A Munk, J Schmidt-Hieber Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence …, 2010 | 25 | 2010 |
| Adaptive wavelet estimation of the diffusion coefficient under additive error measurements M Hoffmann, A Munk, J Schmidt-Hieber Annales de l'IHP Probabilités et statistiques 48 (4), 1186-1216, 2012 | 23 | 2012 |
| Sharp minimax estimation of the variance of Brownian motion corrupted with Gaussian noise TT Cai, A Munk, J Schmidt-Hieber Statistica Sinica, 1011-1024, 2010 | 19 | 2010 |
| Deep relu network approximation of functions on a manifold J Schmidt-Hieber arXiv preprint arXiv:1908.00695, 2019 | 11 | 2019 |
| Minimax theory for a class of nonlinear statistical inverse problems K Ray, J Schmidt-Hieber Inverse Problems 32 (6), 065003, 2016 | 11 | 2016 |
| The Le Cam distance between density estimation, Poisson processes and Gaussian white noise K Ray, J Schmidt-Hieber arXiv preprint arXiv:1608.01824, 2016 | 8 | 2016 |
| Asymptotic equivalence for regression under fractional noise J Schmidt-Hieber The Annals of Statistics 42 (6), 2557-2585, 2014 | 8 | 2014 |
| Nonparametric estimation of the volatility under microstructure noise: wavelet adaptation M Hoffmann, A Munk, J Schmidt-Hieber Available at SSRN 1661906, 2010 | 8 | 2010 |
| Spot volatility estimation for high-frequency data: adaptive estimation in practice T Sabel, J Schmidt-Hieber, A Munk Modeling and stochastic learning for forecasting in high dimensions, 213-241, 2015 | 7 | 2015 |
| Asymptotically efficient estimation of a scale parameter in Gaussian time series and closed-form expressions for the Fisher information T Sabel, J Schmidt-Hieber Bernoulli 20 (2), 747-774, 2014 | 6 | 2014 |
| Nonparametric Bayesian analysis of the compound Poisson prior for support boundary recovery M Reiß, J Schmidt-Hieber Annals of Statistics 48 (3), 1432-1451, 2020 | 5 | 2020 |
| On adaptive posterior contraction rates M Hoffman, J Rousseau, J Schmidt-Hieber Ann. Statist 43 (5), 2259-2295, 2015 | 5 | 2015 |
| Tests for qualitative features in the random coefficients model F Dunker, K Eckle, K Proksch, J Schmidt-Hieber Electronic Journal of Statistics 13 (2), 2257-2306, 2019 | 4 | 2019 |
Axel MunkFelix Bernstein Professor for Mathematical Statistics, Universität Göttingen and Max Planck FellowVerified email at math.uni-goettingen.de
