2 citations · 5 across the 4 of their papers we have counts for
6 papers
Gradient Boosting for Linear Mixed Models
Colin Griesbach, Benjamin Säfken, Elisabeth Waldmann
Gradient boosting from the field of statistical learning is widely known as a powerful framework for estimation and selection of predictor effects in various regression models by a…
Extension of the Gradient Boosting Algorithm for Joint Modeling of Longitudinal and Time-to-Event data
Colin Griesbach, Andreas Mayr, Elisabeth Waldmann
In various data situations joint models are an efficient tool to analyze relationships between time dependent covariates and event times or to correct for event-dependent dropout o…
An update on statistical boosting in biomedicine
Andreas Mayr, Benjamin Hofner, Elisabeth Waldmann +3
Statistical boosting algorithms have triggered a lot of research during the last decade. They combine a powerful machine-learning approach with classical statistical modelling, off…
Bayesian Quantile-Based Joint Modelling of Repeated Measurement and Time-to-Event data, with an Application to Lung Function Decline and Time to Infection in Patients with Cystic Fibrosis
Elisabeth Waldmann, David Taylor-Robinson
Background: The most widely used approach to joint modelling of repeated measurement and time to event data is to combine a linear Gaussian random effects model for the repeated me…
Boosting Joint Models for Longitudinal and Time-to-Event Data
Elisabeth Waldmann, David Taylor-Robinson, Nadja Klein +4
Joint Models for longitudinal and time-to-event data have gained a lot of attention in the last few years as they are a helpful technique to approach common a data structure in cli…
Bayesian Geoadditive Expectile Regression
Elisabeth Waldmann, Fabian Sobotka, Thomas Kneib
Regression classes modeling more than the mean of the response have found a lot of attention in the last years. Expectile regression is a special and computationally convenient cas…