11 citations · 13 across the 3 of their papers we have counts for
5 papers
Errors-In-Variables Model Fitting for Partially Unpaired Data Utilizing Mixture Models
Wolfgang Hoegele, Sarah Brockhaus
We introduce a general framework for regression in the errors-in-variables regime, allowing for full flexibility about the dimensionality of the data, observational error probabili…
Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition
Almond Stöcker, Sarah Brockhaus, Sophia Schaffer +3
We extend Generalized Additive Models for Location, Scale, and Shape (GAMLSS) to regression with functional response. This allows us to simultaneously model point-wise mean curves,…
Boosting Functional Regression Models with FDboost
Sarah Brockhaus, David Rügamer, Sonja Greven
The R add-on package FDboost is a flexible toolbox for the estimation of functional regression models by model-based boosting. It provides the possibility to fit regression models…
Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals
David Rügamer, Sarah Brockhaus, Kornelia Gentsch +2
The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG)…
Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns
Sarah Brockhaus, Andreas Fuest, Andreas Mayr +1
We discuss scalar-on-function regression models where all parameters of the assumed response distribution can be modeled depending on covariates. We thus combine signal regression…