36 citations · 78 across the 14 of their papers we have counts for
6 papers · 1 filter
Estimating Conditional Distributions with Neural Networks using R package deeptrafo
Lucas Kook, Philipp FM Baumann, Oliver Dürr +2
Contemporary empirical applications frequently require flexible regression models for complex response types and large tabular or non-tabular, including image or text, data. Classi…
Deep conditional transformation models for survival analysis
Gabriele Campanella, Lucas Kook, Ida Häggström +2
An every increasing number of clinical trials features a time-to-event outcome and records non-tabular patient data, such as magnetic resonance imaging or text data in the form of…
Distribution-Free Location-Scale Regression
Sandra Siegfried, Lucas Kook, Torsten Hothorn
We introduce a generalized additive model for location, scale, and shape (GAMLSS) next of kin aiming at distribution-free and parsimonious regression modelling for arbitrary outcom…
Deep transformation models for functional outcome prediction after acute ischemic stroke
Lisa Herzog, Lucas Kook, Andrea Götschi +6
In many medical applications, interpretable models with high prediction performance are sought. Often, those models are required to handle semi-structured data like tabular and ima…
Deep interpretable ensembles
Lucas Kook, Andrea Götschi, Philipp FM Baumann +2
Ensembles improve prediction performance and allow uncertainty quantification by aggregating predictions from multiple models. In deep ensembling, the individual models are usually…
Pitfalls and potentials in simulation studies: Questionable research practices in comparative simulation studies allow for spurious claims of superiority of any method
Samuel Pawel, Lucas Kook, Kelly Reeve
Comparative simulation studies are workhorse tools for benchmarking statistical methods. As with other empirical studies, the success of simulation studies hinges on the quality of…