1 citations · 1 across the 1 of their papers we have counts for
4 papers
A comparison of deep networks with ReLU activation function and linear spline-type methods
Konstantin Eckle, Johannes Schmidt-Hieber
Deep neural networks (DNNs) generate much richer function spaces than shallow networks. Since the function spaces induced by shallow networks have several approximation theoretic d…
Tests for qualitative features in the random coefficients model
Fabian Dunker, Konstantin Eckle, Katharina Proksch +1
The random coefficients model is an extension of the linear regression model that allows for unobserved heterogeneity in the population by modeling the regression coefficients as r…
Multiscale inference for multivariate deconvolution
Konstantin Eckle, Nicolai Bissantz, Holger Dette
In this paper we provide new methodology for inference of the geometric features of a multivariate density in deconvolution. Our approach is based on multiscale tests to detect sig…
Multiscale inference for a multivariate density with applications to X-ray astronomy
Konstantin Eckle, Nicolai Bissantz, Holger Dette +2
In this paper we propose methods for inference of the geometric features of a multivariate density. Our approach uses multiscale tests for the monotonicity of the density at arbitr…