Generalized additive models for functional data

作     者：Febrero-Bande, Manuel Gonzalez-Manteiga, Wenceslao 

作者机构：Univ Santiago de Compostela Dpt Stat Santiago De Compostela Spain Univ Santiago de Compostela OR Fac Math Santiago De Compostela Spain 

出 版 物：《TEST》 (验算：西班牙统计与运筹学会杂志)

年 卷 期：2013年第22卷第2期

页      面：278-292页

核心收录：

学科分类：0202[经济学-应用经济学] 02[经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 

基　　金：Ministerio de Ciencia e Innovacion, Spain [MTM2008-03010] Xunta de Galicia, Spain [10MDS207015PR] 

主　　题：Functional data Generalized additive models Generalized linear models 

摘      要：The aim of this paper is to extend the ideas of generalized additive models for multivariate data (with known or unknown link function) to functional data covariates. The proposed algorithm is a modified version of the local scoring and backfitting algorithms that allows for the nonparametric estimation of the link function. This algorithm would be applied to predict a binary response example.