Generalized additive models for functional data

被引：47

作者：

Febrero-Bande, Manuel ^{[1
,2
]}

Gonzalez-Manteiga, Wenceslao ^{[1
,2
]}

机构：

[1] Univ Santiago de Compostela, Dpt Stat, Santiago De Compostela, Spain

[2] Univ Santiago de Compostela, OR Fac Math, Santiago De Compostela, Spain

来源：

TEST | 2013年 / 22卷 / 02期

关键词：

Functional data; Generalized additive models; Generalized linear models; LINEAR-MODELS; REGRESSION; SINGLE;

D O I：

10.1007/s11749-012-0308-0

中图分类号：

O21 [概率论与数理统计]; C8 [统计学];

学科分类号：

020208 ; 070103 ; 0714 ;

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.

引用

页码：278 / 292

页数：15

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