4 papers · 1 filter
Data-Efficient Generative Modeling of Non-Gaussian Global Climate Fields via Scalable Composite Transformations
Johannes Brachem, Paul F. V. Wiemann, Matthias Katzfuss
Quantifying uncertainty in climate-model output requires characterizing internal variability, often through large ensembles of physical climate-model runs. Since each additional en…
Bayesian Penalized Transformation Models: Structured Additive Location-Scale Regression for Arbitrary Conditional Distributions
Johannes Brachem, Paul F. V. Wiemann, Thomas Kneib
Penalized transformation models (PTMs) are a semiparametric location-scale regression family that estimate a response's conditional distribution directly from the data, and model t…
Bayesian structured additive quantile regression for inflated bounded data
Francisco F. Queiroz, Johannes Brachem, Paul F. V. Wiemann +1
Bounded continuous data on the unit interval frequently arise in applied fields and often exhibit a non-negligible proportion of observations at the boundaries. Inflated regression…
Graphical Transformation Models
Matthias Herp, Johannes Brachem, Michael Altenbuchinger +1
Graphical Transformation Models (GTMs) are introduced as a novel approach to effectively model multivariate data with intricate marginals and complex dependency structures semipara…