文章基本信息

标题：Boosting Functional Regression Models with FDboost
本地全文：下载
作者：Sarah Brockhaus ; David Rügamer ; Sonja Greven 等
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2020
卷号：94
期号：1
页码：1-50
DOI：10.18637/jss.v094.i10
出版社：University of California, Los Angeles
摘要：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.
关键词：functional data analysis;function-on-function regression;function-on-scalar regression;gradient boosting;model-based boosting;scalar-on-function regression.
其他关键词：functional data analysis;function-on-function regression;function-on-scalar regression;gradient boosting;model-based boosting;scalar-on-function regression