138 citations · 532 across the 35 of their papers we have counts for
6 papers · 1 filter
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…
Generalised Joint Regression for Count Data with a Focus on Modelling Football Matches
Hendrik van der Wurp, Andreas Groll, Thomas Kneib +2
We propose a versatile joint regression framework for count responses. The method is implemented in the R add-on package GJRM and allows for modelling linear and non-linear depende…
Treatment effects beyond the mean using GAMLSS
Maike Hohberg, Peter Pütz, Thomas Kneib
This paper introduces distributional regression, also known as generalized additive models for location, scale and shape (GAMLSS), as a modeling framework for analyzing treatment e…
Bayesian Measurement Error Correction in Structured Additive Distributional Regression with an Application to the Analysis of Sensor Data on Soil-Plant Variability
Alessio Pollice, Giovanna Jona Lasinio, Roberta Rossi +3
The flexibility of the Bayesian approach to account for covariates with measurement error is combined with semiparametric regression models for a class of continuous, discrete and…
Bayesian structured additive distributional regression with an application to regional income inequality in Germany
Nadja Klein, Thomas Kneib, Stefan Lang +1
We propose a generic Bayesian framework for inference in distributional regression models in which each parameter of a potentially complex response distribution and not only the me…
Maximum penalized likelihood estimation in semiparametric capture-recapture models
Théo Michelot, Roland Langrock, Thomas Kneib +1
We discuss the semiparametric modeling of mark-recapture-recovery data where the temporal and/or individual variation of model parameters is explained via covariates. Typically, in…