activity
20152026
most citedNon Proportional Odds Models are Widely Dispensable -- Sparser Modeling based on Parametric and Additive Location-Shift Approaches

2 citations · 4 across the 11 of their papers we have counts for

collaborators

14 papers

stat.ME2026

A tool to determine the degrees of freedom in tree-structured varying coefficient models

Nikolai Spuck, Moritz Berger

The tree-structured varying coefficient (TSVC) model is a flexible approach for generalized regression, where the linear effects of the covariates are allowed to vary with the valu…

stat.ME2026

A Simulation Study to Compare Inferential Properties when Modelling Ordinal Outcomes: The Case for the (Plain but Robust) Proportional Odds Model

Stefan Inerle, Markus Pauly, Moritz Berger

Ordinal measurements are common outcomes in studies within psychology, as well as in the social and behavioral sciences. Choosing an appropriate regression model for analysing such…

stat.ME2023

Modeling the Ratio of Correlated Biomarkers Using Copula Regression

Moritz Berger, Nadja Klein, Michael Wagner +1

Modeling the ratio of two dependent components as a function of covariates is a frequently pursued objective in observational research. Despite the high relevance of this topic in…

stat.ME2020★ 2 cited

Non Proportional Odds Models are Widely Dispensable -- Sparser Modeling based on Parametric and Additive Location-Shift Approaches

Gerhard Tutz, Moritz Berger

The potential of location-shift models to find adequate models between the proportional odds model and the non-proportional odds model is investigated. It is demonstrated that thes…

stat.ME2020

Transition Models for Count Data: a Flexible Alternative to Fixed Distribution Models

Moritz Berger, Gerhard Tutz

A flexible semiparametric class of models is introduced that offers an alternative to classical regression models for count data as the Poisson and negative binomial model, as well…

stat.ME2020

Assessing the Calibration of Subdistribution Hazard Models in Discrete Time

Moritz Berger, Matthias Schmid

The generalization performance of a risk prediction model can be evaluated by its calibration, which measures the agreement between predicted and observed outcomes on external vali…