b'Nadja Klein'

research

∙ 07/20/2023

Distributional Regression for Data Analysis

Flexible modeling of how an entire distribution changes with covariates ...

0 Nadja Klein, et al. ∙

research

∙ 06/05/2023

Truly Multivariate Structured Additive Distributional Regression

Generalized additive models for location, scale and shape (GAMLSS) are a...

0 Lucas Kock, et al. ∙

research

∙ 05/19/2023

The Deep Promotion Time Cure Model

We propose a novel method for predicting time-to-event in the presence o...

0 Victor Medina-Olivares, et al. ∙

research

∙ 05/11/2023

Dropout Regularization in Extended Generalized Linear Models based on Double Exponential Families

Even though dropout is a popular regularization technique, its theoretic...

0 Benedikt Lütke Schwienhorst, et al. ∙

research

∙ 01/13/2023

Scalable Estimation for Structured Additive Distributional Regression

Recently, fitting probabilistic models have gained importance in many ar...

0 Nikolaus Umlauf, et al. ∙

research

∙ 12/03/2022

Accounting for Time Dependency in Meta-Analyses of Concordance Probability Estimates

Recent years have seen the development of many novel scoring tools for d...

0 Matthias Schmid, et al. ∙

research

∙ 11/29/2022

Anisotropic multidimensional smoothing using Bayesian tensor product P-splines

We introduce a highly efficient fully Bayesian approach for anisotropic ...

0 Paul Bach, et al. ∙

research

∙ 11/01/2022

Informed Priors for Knowledge Integration in Trajectory Prediction

Informed machine learning methods allow the integration of prior knowled...

0 Christian Schlauch, et al. ∙

research

∙ 10/19/2022

Distributional Adaptive Soft Regression Trees

Random forests are an ensemble method relevant for many problems, such a...

0 Nikolaus Umlauf, et al. ∙

research

∙ 07/18/2022

Boosting Multivariate Structured Additive Distributional Regression Models

We develop a model-based boosting approach for multivariate distribution...

0 Annika Strömer, et al. ∙

research

∙ 04/29/2022

Incorporating Actor Heterogeneity into Large Network Models through Variational Approximations

The analysis of network data has gained considerable interest in the rec...

0 Nadja Klein, et al. ∙

research

∙ 02/25/2022

Boosting Distributional Copula Regression

Capturing complex dependence structures between outcome variables (e.g.,...

0 Nicolai Hans, et al. ∙

research

∙ 02/03/2022

Deselection of Base-Learners for Statistical Boosting – with an Application to Distributional Regression

We present a new procedure for enhanced variable selection for component...

0 Annika Strömer, et al. ∙

research

∙ 10/03/2021

Marginally calibrated response distributions for end-to-end learning in autonomous driving

End-to-end learners for autonomous driving are deep neural networks that...

0 Clara Hoffmann, et al. ∙

research

∙ 09/09/2021

Posterior Concentration Rates for Bayesian O'Sullivan Penalized Splines

The O'Sullivan penalized splines approach is a popular frequentist appro...

0 Paul Bach, et al. ∙

research

∙ 06/07/2021

A multivariate Gaussian random field prior against spatial confounding

Spatial models are used in a variety research areas, such as environment...

0 Isa Marques, et al. ∙

research

∙ 05/23/2021

Bayesian Effect Selection for Additive Quantile Regression with an Analysis to Air Pollution Thresholds

Statistical techniques used in air pollution modelling usually lack the ...

0 Nadja Klein, et al. ∙

research

∙ 05/19/2021

Flexible Specification Testing in Semi-Parametric Quantile Regression Models

We propose several novel consistent specification tests for quantile reg...

0 Tim Kutzker, et al. ∙

research

∙ 05/04/2021

Variational Inference and Sparsity in High-Dimensional Deep Gaussian Mixture Models

Gaussian mixture models are a popular tool for model-based clustering, a...

0 Lucas Kock, et al. ∙

research

∙ 04/29/2021

Bivariate Analysis of Birth Weight and Gestational Age Depending on Environmental Exposures: Bayesian Distributional Regression with Copulas

In this article, we analyze perinatal data with birth weight (BW) as pri...

0 Jonathan Rathjens, et al. ∙

research

∙ 04/06/2021

deepregression: a Flexible Neural Network Framework for Semi-Structured Deep Distributional Regression

This paper describes the implementation of semi-structured deep distribu...

0 David Rügamer, et al. ∙

research

∙ 12/20/2020

Bayesian Conditional Transformation Models

Recent developments in statistical regression methodology establish flex...

0 Manuel Carlan, et al. ∙

research

∙ 10/05/2020

Deep Distributional Time Series Models and the Probabilistic Forecasting of Intraday Electricity Prices

Recurrent neural networks (RNNs) with rich feature vectors of past value...

0 Nadja Klein, et al. ∙

research

∙ 02/13/2020

A Unifying Network Architecture for Semi-Structured Deep Distributional Learning

We propose a unifying network architecture for deep distributional learn...

22 David Rügamer, et al. ∙

research

∙ 11/20/2019

Assessment and adjustment of approximate inference algorithms using the law of total variance

A common method for assessing validity of Bayesian sampling or approxima...

0 Xuejun Yu, et al. ∙

research

∙ 09/25/2019

bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)

Over the last decades, the challenges in applied regression and in predi...

0 Nikolaus Umlauf, et al. ∙

research

∙ 09/03/2019

In Search of Lost Edges: A Case Study on Reconstructing Financial Networks

To capture the systemic complexity of international financial systems, n...

0 Michael Lebacher, et al. ∙

research

∙ 08/26/2019

Marginally-calibrated deep distributional regression

Deep neural network (DNN) regression models are widely used in applicati...

21 Nadja Klein, et al. ∙

research

∙ 07/10/2019

Bayesian Variable Selection for Non-Gaussian Responses: A Marginally Calibrated Copula Approach

We propose a new highly flexible and tractable Bayesian approach to unde...

0 Nadja Klein, et al. ∙

research

∙ 07/10/2019

Bayesian Inference for Regression Copulas

We propose a new semi-parametric distributional regression smoother for ...

0 Michael Stanley Smith, et al. ∙

research

∙ 06/07/2019

Multivariate Conditional Transformation Models

Regression models describing the joint distribution of multivariate resp...

0 Nadja Klein, et al. ∙

research

∙ 02/27/2019

Bayesian Effect Selection in Structured Additive Distributional Regression Models

We propose a novel spike and slab prior specification with scaled beta p...

0 Nadja Klein, et al. ∙

research

∙ 05/18/2018

More green space is related to less antidepressant prescription rates in the Netherlands: A Bayesian geoadditive quantile regression approach

Exposure to green space seems to be beneficial for self-reported mental ...

0 Marco Helbich, et al. ∙

research

∙ 04/27/2018

Implicit Copulas from Bayesian Regularized Regression Smoothers

We show how to extract the implicit copula of a response vector from a B...

0 Nadja Klein, et al. ∙

research

∙ 09/09/2016

Boosting Joint Models for Longitudinal and Time-to-Event Data

Joint Models for longitudinal and time-to-event data have gained a lot o...

0 Elisabeth Waldmann, et al. ∙

Nadja Klein

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