Johannes Schmidt-Hieber
Johannes Schmidt-Hieber
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Bayesian linear regression with sparse priors
I Castillo, J Schmidt-Hieber, A Van der Vaart
Annals of Statistics 43 (5), 1986-2018, 2015
Nonparametric regression using deep neural networks with ReLU activation function
J Schmidt-Hieber
Annals of Statistics 48 (4), 1875-1897, 2020
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
On adaptive posterior concentration rates
M Hoffmann, J Rousseau, J Schmidt-Hieber
Annals of Statistics 43 (5), 2259-2295, 2015
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
Multiscale methods for shape constraints in deconvolution: confidence statements for qualitative features
J Schmidt-Hieber, A Munk, L Dümbgen
Annals of statistics 41 (3), 1299-1328, 2013
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
Lower bounds for volatility estimation in microstructure noise models
A Munk, J Schmidt-Hieber
Borrowing Strength: Theory Powering Applications–A Festschrift for Lawrence …, 2010
Deep relu network approximation of functions on a manifold
J Schmidt-Hieber
arXiv preprint arXiv:1908.00695, 2019
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
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
Minimax theory for a class of nonlinear statistical inverse problems
K Ray, J Schmidt-Hieber
Inverse Problems 32 (6), 065003, 2016
Asymptotic equivalence for regression under fractional noise
J Schmidt-Hieber
Annals of Statistics 42 (6), 2557-2585, 2014
Nonparametric estimation of the volatility under microstructure noise: wavelet adaptation
M Hoffmann, A Munk, J Schmidt-Hieber
Available at SSRN 1661906, 2010
The Le Cam distance between density estimation, Poisson processes and Gaussian white noise
K Ray, J Schmidt-Hieber
Mathematical Statistics and Learning 1 (2), 101-170, 2018
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
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
The Kolmogorov–Arnold representation theorem revisited
J Schmidt-Hieber
Neural Networks 137, 119-126, 2021
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
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
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