Direkt zum InhaltDirekt zur SucheDirekt zur Navigation
▼ Zielgruppen ▼

Humboldt-Universität zu Berlin - Statistics

Humboldt-Universität zu Berlin | School of Business and Economics | Statistics | News | Paper "Boosting Functional Regression Models with FDboost" by Brockhaus, Rügamer and Greven has appeared in Journal of Statistical Software 94(10)

Paper "Boosting Functional Regression Models with FDboost" by Brockhaus, Rügamer and Greven has appeared in Journal of Statistical Software 94(10)

The paper Boosting Functional Regression Models with FDboost by S. Brockhaus, D. Rügamer and S. Greven, has been appeared in  Journal of Statistical Software, Vol. 94(10), pp. 1-50,

Abstract

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 functional covariates, i.e., scalar-on-function, function-on-scalar and function-on-function regression models. In addition to mean regression, quantile regression models as well as generalized additive models for location scale and shape can be fitted with FDboost. Furthermore, boosting can be used in high-dimensional data settings with more covariates than observations. We provide a hands-on tutorial on model fitting and tuning, including the visualization of results. The methods for scalar-on-function regression are illustrated with spectrometric data of fossil fuels and those for functional response regression with a data set including bioelectrical signals for emotional episodes.