239 citations · 1k across the 28 of their papers we have counts for
11 papers · 1 filter
Subgroup detection in linear growth curve models with generalized linear mixed model (GLMM) trees
Marjolein Fokkema, Achim Zeileis
Growth curve models are popular tools for studying the development of a response variable within subjects over time. Heterogeneity between subjects is common in such models, and re…
Tree models for assessing covariate-dependent method agreement
Siranush Karapetyan, Achim Zeileis, André Henriksen +1
Method comparison studies explore the agreement of measurements made by two or more methods. Commonly, agreement is evaluated by the well-established Bland-Altman analysis. However…
What Makes Forest-Based Heterogeneous Treatment Effect Estimators Work?
Susanne Dandl, Torsten Hothorn, Heidi Seibold +3
Estimation of heterogeneous treatment effects (HTE) is of prime importance in many disciplines, ranging from personalized medicine to economics among many others. Random forests ha…
Cholesky-based multivariate Gaussian regression
Thomas Muschinski, Georg J. Mayr, Thorsten Simon +2
Distributional regression is extended to Gaussian response vectors of dimension greater than two by parameterizing the covariance matrix of the response distribution using the…
Bias Reduction as a Remedy to the Consequences of Infinite Estimates in Poisson and Tobit Regression
Susanne Köll, Ioannis Kosmidis, Christian Kleiber +1
Data separation is a well-studied phenomenon that can cause problems in the estimation and inference from binary response models. Complete or quasi-complete separation occurs when…
Circular Regression Trees and Forests with an Application to Probabilistic Wind Direction Forecasting
Moritz N. Lang, Lisa Schlosser, Torsten Hothorn +3
While circular data occur in a wide range of scientific fields, the methodology for distributional modeling and probabilistic forecasting of circular response variables is rather l…
The Power of Unbiased Recursive Partitioning: A Unifying View of CTree, MOB, and GUIDE
Lisa Schlosser, Torsten Hothorn, Achim Zeileis
A core step of every algorithm for learning regression trees is the selection of the best splitting variable from the available covariates and the corresponding split point. Early…
Distributional Regression Forests for Probabilistic Precipitation Forecasting in Complex Terrain
Lisa Schlosser, Torsten Hothorn, Reto Stauffer +1
To obtain a probabilistic model for a dependent variable based on some set of explanatory variables, a distributional approach is often adopted where the parameters of the distribu…
Transformation Forests
Torsten Hothorn, Achim Zeileis
Regression models for supervised learning problems with a continuous target are commonly understood as models for the conditional mean of the target given predictors. This notion i…
Model-based Recursive Partitioning for Subgroup Analyses
Heidi Seibold, Achim Zeileis, Torsten Hothorn
The identification of patient subgroups with differential treatment effects is the first step towards individualised treatments. A current draft guideline by the EMA discusses pote…
Individual Treatment Effect Prediction for ALS Patients
Heidi Seibold, Achim Zeileis, Torsten Hothorn
A treatment for a complicated disease may be helpful for some but not all patients, which makes predicting the treatment effect for new patients important yet challenging. Here we…