36 citations · 65 across the 10 of their papers we have counts for
11 papers
Refuting "Debunking the GAMLSS Myth: Simplicity Reigns in Pulmonary Function Diagnostics"
Robert A. Rigby, Mikis D. Stasinopoulos, Achim Zeileis +7
We read with interest the above article by Zavorsky (2025, Respiratory Medicine, doi:10.1016/j.rmed.2024.107836) concerning reference equations for pulmonary function testing. The…
Simultaneous Estimation and Model Choice for Big Discrete Time-to-Event Data with Additive Predictors
Benjamin Müller, Nikolaus Umlauf, Johannes Seiler +2
Discrete-time hazard models are widely used when event times are measured in intervals or are not precisely observed. While these models can be estimated using standard generalized…
Generalized Multivariate Functional Additive Mixed Models for Location, Scale, and Shape
Alexander Volkmann, Nikolaus Umlauf, Sonja Greven
We propose a flexible regression framework to model the conditional distribution of multilevel generalized multivariate functional data of potentially mixed type, e.g. binary and c…
Stagewise Boosting Distributional Regression
Mattias Wetscher, Johannes Seiler, Reto Stauffer +1
Forward stagewise regression is a simple algorithm that can be used to estimate regularized models. The updating rule adds a small constant to a regression coefficient in each iter…
Flexible joint models for multivariate longitudinal and time-to-event data using multivariate functional principal components
Alexander Volkmann, Nikolaus Umlauf, Sonja Greven
The joint modeling of multiple longitudinal biomarkers together with a time-to-event outcome is a challenging modeling task of continued scientific interest. In particular, the com…
Scalable Estimation for Structured Additive Distributional Regression
Nikolaus Umlauf, Johannes Seiler, Mattias Wetscher +3
Recently, fitting probabilistic models have gained importance in many areas but estimation of such distributional models with very large data sets is a difficult task. In particula…
Distributional Adaptive Soft Regression Trees
Nikolaus Umlauf, Nadja Klein
Random forests are an ensemble method relevant for many problems, such as regression or classification. They are popular due to their good predictive performance (compared to, e.g.…
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…
bamlss: A Lego Toolbox for Flexible Bayesian Regression (and Beyond)
Nikolaus Umlauf, Nadja Klein, Thorsten Simon +1
Over the last decades, the challenges in applied regression and in predictive modeling have been changing considerably: (1) More flexible model specifications are needed as big(ger…
Nonlinear association structures in flexible Bayesian additive joint models
Meike Köhler, Nikolaus Umlauf, Sonja Greven
Joint models of longitudinal and survival data have become an important tool for modeling associations between longitudinal biomarkers and event processes. The association between…
Flexible Bayesian additive joint models with an application to type 1 diabetes research
Meike Köhler, Nikolaus Umlauf, Andreas Beyerlein +3
The joint modeling of longitudinal and time-to-event data is an important tool of growing popularity to gain insights into the association between a biomarker and an event process.…