Výsledky vyhledávání - "Brockhaus, Sarah"

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Errors-In-Variables Model Fitting for Partially Unpaired Data Utilizing Mixture Models

Autor: Hoegele, Wolfgang, Brockhaus, Sarah

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 probability density types, the (nonlinear) model type and the avoidance of ad-h

Externí odkaz: http://arxiv.org/abs/2406.18154

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Boosting Functional Response Models for Location, Scale and Shape with an Application to Bacterial Competition

Autor: Stöcker, Almond, Brockhaus, Sarah, Schaffer, Sophia, von Bronk, Benedikt, Opitz, Madeleine, Greven, Sonja

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, variances and other distributional parameters of the response in depe

Externí odkaz: http://arxiv.org/abs/1809.09881

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Boosting Functional Regression Models with FDboost

Autor: Brockhaus, Sarah, Rügamer, David, Greven, Sonja

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 for scalar and functional response with effects of scalar as well as f

Externí odkaz: http://arxiv.org/abs/1705.10662

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Boosting Factor-Specific Functional Historical Models for the Detection of Synchronisation in Bioelectrical Signals

Autor: Rügamer, David, Brockhaus, Sarah, Gentsch, Kornelia, Scherer, Klaus, Greven, Sonja

The link between different psychophysiological measures during emotion episodes is not well understood. To analyse the functional relationship between electroencephalography (EEG) and facial electromyography (EMG), we apply historical function-on-fun

Externí odkaz: http://arxiv.org/abs/1609.06070

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Signal Regression Models for Location, Scale and Shape with an Application to Stock Returns

Autor: Brockhaus, Sarah, Fuest, Andreas, Mayr, Andreas, Greven, Sonja

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 models with generalized additive models for location, scale and shape

Externí odkaz: http://arxiv.org/abs/1605.04281

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