138 citations · 302 across the 32 of their papers we have counts for
8 papers · 1 filter
Marginally calibrated response distributions for end-to-end learning in autonomous driving
Clara Hoffmann, Nadja Klein
End-to-end learners for autonomous driving are deep neural networks that predict the instantaneous steering angle directly from images of the ahead-lying street. These learners mus…
Posterior Concentration Rates for Bayesian Penalized Splines
Paul Bach, Nadja Klein
Despite their widespread use in practice, the asymptotic properties of Bayesian penalized splines have not been investigated so far. We close this gap and study posterior concentra…
A multivariate Gaussian random field prior against spatial confounding
Isa Marques, Thomas Kneib, Nadja Klein
Spatial models are used in a variety research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in many spatial regression models is spatial conf…
Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds
Nadja Klein, Jorge Mateu
Statistical techniques used in air pollution modelling usually lack the possibility to understand which predictors affect air pollution in which functional form; and are not able t…
Flexible Specification Testing in Quantile Regression Models
Tim Kutzker, Nadja Klein, Dominik Wied
We propose three novel consistent specification tests for quantile regression models which generalize former tests in three ways. First, we allow the covariate effects to be quanti…
Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models
Lucas Kock, Nadja Klein, David J. Nott
Gaussian mixture models are a popular tool for model-based clustering, and mixtures of factor analyzers are Gaussian mixture models having parsimonious factor covariance structure…
Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas
Jonathan Rathjens, Arthur Kolbe, Jürgen Hölzer +2
In this article, we analyze perinatal data with birth weight (BW) as primarily interesting response variable. Gestational age (GA) is usually an important covariate and included in…
deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression
David Rügamer, Chris Kolb, Cornelius Fritz +11
In this paper we describe the implementation of semi-structured deep distributional regression, a flexible framework to learn conditional distributions based on the combination of…