10 citations · 17 across the 2 of their papers we have counts for
6 papers
Combining Graph Neural Networks and Spatio-temporal Disease Models to Predict COVID-19 Cases in Germany
Cornelius Fritz, Emilio Dorigatti, David Rügamer
During 2020, the infection rate of COVID-19 has been investigated by many scholars from different research fields. In this context, reliable and interpretable forecasts of disease…
Semi-Structured Deep Piecewise Exponential Models
Philipp Kopper, Sebastian Pölsterl, Christian Wachinger +3
We propose a versatile framework for survival analysis that combines advanced concepts from statistics with deep learning. The presented framework is based on piecewise exponential…
Neural Mixture Distributional Regression
David Rügamer, Florian Pfisterer, Bernd Bischl
We present neural mixture distributional regression (NMDR), a holistic framework to estimate complex finite mixtures of distributional regressions defined by flexible additive pred…
Deep Conditional Transformation Models
Philipp F. M. Baumann, Torsten Hothorn, David Rügamer
Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set of features remains challenging, especially in high-dimensional settings. Conditiona…
A General Machine Learning Framework for Survival Analysis
Andreas Bender, David Rügamer, Fabian Scheipl +1
The modeling of time-to-event data, also known as survival analysis, requires specialized methods that can deal with censoring and truncation, time-varying features and effects, an…
Conditional Model Selection in Mixed-Effects Models with cAIC4
Benjamin Säfken, David Rügamer, Thomas Kneib +1
Model selection in mixed models based on the conditional distribution is appropriate for many practical applications and has been a focus of recent statistical research. In this pa…