138 citations · 532 across the 35 of their papers we have counts for
38 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…
Probabilistic Topic Modelling with Transformer Representations
Arik Reuter, Anton Thielmann, Christoph Weisser +2
Topic modelling was mostly dominated by Bayesian graphical models during the last decade. With the rise of transformers in Natural Language Processing, however, several successful…
Demystifying Spatial Confounding
Emiko Dupont, Isa Marques, Thomas Kneib
Spatial confounding is a fundamental issue in spatial regression models which arises because spatial random effects, included to approximate unmeasured spatial variation, are typic…
"Spatial Joint Models through Bayesian Structured Piece-wise Additive Joint Modelling for Longitudinal and Time-to-Event Data"
Anja Rappl, Thomas Kneib, Stefan Lang +1
Joint models for longitudinal and time-to-event data have seen many developments in recent years. Though spatial joint models are still rare and the traditional proportional hazard…
Neural Additive Models for Location Scale and Shape: A Framework for Interpretable Neural Regression Beyond the Mean
Anton Thielmann, René-Marcel Kruse, Thomas Kneib +1
Deep neural networks (DNNs) have proven to be highly effective in a variety of tasks, making them the go-to method for problems requiring high-level predictive power. Despite this…
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…
Multivariate Distributional Stochastic Frontier Models
Rouven Schmidt, Thomas Kneib
The primary objective of Stochastic Frontier (SF) Analysis is the deconvolution of the estimated composed error terms into noise and inefficiency. Assuming a parametric production…
Twitmo: A Twitter Data Topic Modeling and Visualization Package for R
Andreas Buchmüller, Gillian Kant, Christoph Weisser +3
We present Twitmo, a package that provides a broad range of methods to collect, pre-process, analyze and visualize geo-tagged Twitter data. Twitmo enables the user to collect geo-t…
Bayesian Discrete Conditional Transformation Models
Manuel Carlan, Thomas Kneib
We propose a novel Bayesian model framework for discrete ordinal and count data based on conditional transformations of the responses. The conditional transformation function is es…
Distributional Gradient Boosting Machines
Alexander März, Thomas Kneib
We present a unified probabilistic gradient boosting framework for regression tasks that models and predicts the entire conditional distribution of a univariate response variable a…
Using the Softplus Function to Construct Alternative Link Functions in Generalized Linear Models and Beyond
Paul F. V. Wiemann, Thomas Kneib, Julien Hambuckers
Response functions linking regression predictors to properties of the response distribution are fundamental components in many statistical models. However, the choice of these func…
Probabilistic Time Series Forecasts with Autoregressive Transformation Models
David Rügamer, Philipp F. M. Baumann, Thomas Kneib +1
Probabilistic forecasting of time series is an important matter in many applications and research fields. In order to draw conclusions from a probabilistic forecast, we must ensure…
A multivariate Gaussian random field prior against spatial confounding
Isa Marques, Thomas Kneib, Nadja Klein
Spatial models are used in a variety research areas, such as environmental sciences, epidemiology, or physics. A common phenomenon in many spatial regression models is spatial conf…
Flexible Bayesian Modeling of Counts: Constructing Penalized Complexity Priors
Mahsa Nadifar, Hossein Baghishani, Thomas Kneib +1
Many of the data, particularly in medicine and disease mapping are count. Indeed, the under or overdispersion problem in count data distrusts the performance of the classical Poiss…
Adaptive shrinkage of smooth functional effects towards a predefined functional subspace
Paul Wiemann, Thomas Kneib
In this paper, we propose a new horseshoe-type prior hierarchy for adaptively shrinking spline-based functional effects towards a predefined vector space of parametric functions. I…
Bayesian Conditional Transformation Models
Manuel Carlan, Thomas Kneib, Nadja Klein
Recent developments in statistical regression methodology shift away from pure mean regression towards distributional regression models. One important strand thereof is that of con…
Beyond unidimensional poverty analysis using distributional copula models for mixed ordered-continuous outcomes
Maike Hohberg, Francesco Donat, Giampiero Marra +1
Poverty is a multidimensional concept often comprising a monetary outcome and other welfare dimensions such as education, subjective well-being or health, that are measured on an o…
Analytic expressions for the Cumulative Distribution Function of the Composed Error Term in Stochastic Frontier Analysis with Truncated Normal and Exponential Inefficiencies
Rouven Schmidt, Thomas Kneib
In the stochastic frontier model, the composed error term consists of the measurement error and the inefficiency term. A general assumption is that the inefficiency term follows a…
Noncrossing structured additive multiple-output Bayesian quantile regression models
Bruno Santos, Thomas Kneib
Quantile regression models are a powerful tool for studying different points of the conditional distribution of univariate response variables. Their multivariate counterpart extens…
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…
Multivariate Conditional Transformation Models
Nadja Klein, Torsten Hothorn, Luisa Barbanti +1
Regression models describing the joint distribution of multivariate response variables conditional on covariate information have become an important aspect of contemporary regressi…
Bayesian Effect Selection in Structured Additive Distributional Regression Models
Nadja Klein, Manuel Carlan, Thomas Kneib +2
We propose a novel spike and slab prior specification with scaled beta prime marginals for the importance parameters of regression coefficients to allow for general effect selectio…
Lost in translation: On the impact of data coding on penalized regression with interactions
Johannes W R Martini, Francisco Rosales, Ngoc-Thuy Ha +3
Penalized regression approaches are standard tools in quantitative genetics. It is known that the fit of an \emph{ordinary least squares} (OLS) regression is independent of certain…
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…