6 papers
Reconciling Interpretability with Covariate-Dependent Shape Flexibility in Penalized Transformation Models for Distributional Regression
Johannes Brachem, Thomas Kneib
A central challenge in distributional regression is to allow the shape of the conditional distribution of the response variable to vary flexibly with covariates while retaining dir…
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…
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…
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…
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…
Liesel: A Python Framework for Graph-Based Bayesian Modeling and Customizable MCMC with Support for Generalized Additive Models
Hannes Riebl, Johannes Brachem, Thomas Kneib +2
Liesel is a Python framework for Bayesian model building and posterior computation with dedicated support for generalized additive regression models that is designed to reduce fric…