138 citations · 302 across the 32 of their papers we have counts for
34 papers
Copulas for Geostatistical Data: Foundations, Modeling Principles and Statistical Inference
Axel Bücher, Nadja Klein
Spatial statistics commonly describes spatial dependence through second-order quantities such as covariance functions and variograms, often within Gaussian random-field models and…
Truly Multivariate Structured Additive Distributional Regression
Lucas Kock, Nadja Klein
Generalized additive models for location, scale and shape (GAMLSS) are a popular extension to mean regression models where each parameter of an arbitrary distribution is modelled t…
The Deep Promotion Time Cure Model
Victor Medina-Olivares, Stefan Lessmann, Nadja Klein
We propose a novel method for predicting time-to-event in the presence of cure fractions based on flexible survivals models integrated into a deep neural network framework. Our app…
Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families
Benedikt Lütke Schwienhorst, Lucas Kock, Nadja Klein +1
Even though dropout is a popular regularization technique, its theoretical properties are not fully understood. In this paper we study dropout regularization in extended generalize…
Scalable Estimation for Structured Additive Distributional Regression
Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher +3
Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task. In particula…
Accounting for Time Dependency in Meta-Analyses of Concordance Probability Estimates
Matthias Schmid, Tim Friede, Nadja Klein +1
Recent years have seen the development of many novel scoring tools for disease prognosis and prediction. To become accepted for use in clinical applications, these tools have to be…
Distributional Adaptive Soft Regression Trees
Nikolaus Umlauf, Nadja Klein
Random forests are an ensemble method relevant for many problems, such as regression or classification. They are popular due to their good predictive performance (compared to, e.g.…
Boosting Multivariate Structured Additive Distributional Regression Models
Annika Strömer, Nadja Klein, Christian Staerk +2
We develop a model-based boosting approach for multivariate distributional regression within the framework of generalized additive models for location, scale, and shape. Our approa…
Actor Heterogeneity and Explained Variance in Network Models -- A Scalable Approach through Variational Approximations
Nadja Klein, Göran Kauermann
The analysis of network data has gained considerable interest in recent years. This also includes the analysis of large, high-dimensional networks with hundreds and thousands of no…
Boosting Distributional Copula Regression
Nicolai Hans, Nadja Klein, Florian Faschingbauer +2
Capturing complex dependence structures between outcome variables (e.g., study endpoints) is of high relevance in contemporary biomedical data problems and medical research. Distri…
Deselection of Base-Learners for Statistical Boosting -- with an Application to Distributional Regression
Annika Strömer, Christian Staerk, Nadja Klein +3
We present a new procedure for enhanced variable selection for component-wise gradient boosting. Statistical boosting is a computational approach that emerged from machine learning…
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…
Bayesian Conditional Transformation Models
Manuel Carlan, Thomas Kneib, Nadja Klein
Recent developments in statistical regression methodology shift away from pure mean regression towards distributional regression models. One important strand thereof is that of con…
Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices
Nadja Klein, Michael Stanley Smith, David J. Nott
Recurrent neural networks (RNNs) with rich feature vectors of past values can provide accurate point forecasts for series that exhibit complex serial dependence. We propose two app…
Semi-Structured Distributional Regression -- Extending Structured Additive Models by Arbitrary Deep Neural Networks and Data Modalities
David Rügamer, Chris Kolb, Nadja Klein
Combining additive models and neural networks allows to broaden the scope of statistical regression and extend deep learning-based approaches by interpretable structured additive p…
Assessment and adjustment of approximate inference algorithms using the law of total variance
Xuejun Yu, David J. Nott, Minh-Ngoc Tran +1
A common method for assessing validity of Bayesian sampling or approximate inference methods makes use of simulated data replicates for parameters drawn from the prior. Under conti…
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Nikolaus Umlauf, Nadja Klein, Thorsten Simon +1
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger…