138 citations · 532 across the 35 of their papers we have counts for
3 papers · 1 filter
Demystifying Spatial Confounding
Emiko Dupont, Isa Marques, Thomas Kneib
Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random effects, included to approximate unmeasured spatial variation, are typic…
"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"
Anja Rappl, Thomas Kneib, Stefan Lang +1
Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazard…
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Anton Thielmann, René-Marcel Kruse, Thomas Kneib +1
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this…