Linear regression models for functional data

被引：12
|
作者：
Cardot, Herve [1 ]
Sarda, Pascal [2 ,3 ]
机构：
[1] INRA, Unite Biometrie & Intelligence Artificielle, BP 27, F-31326 Castanet Tolosan, France
[2] Univ Toulouse 3, LSP, UMR C5583, F-31062 Toulouse, France
[3] Univ Toulouse le Mirail, GRIMM, EA2254, F-31058 Toulouse, France
来源：
ART OF SEMIPARAMETRICS | 2006年
关键词：
functional linear model; generalized functional linear model; conditional quantiles; regularization; roughness penalty; splines;
D O I：
10.1007/3-7908-1701-5_4
中图分类号：
O29 [应用数学];
学科分类号：
070104 ;
摘要：
This paper addresses a specific case of regression analysis: the predictor is a random curve and the response is a scalar. We consider three models: the functional linear model, the functional generalized linear model and functional linear regression on quantiles. Spline functions are used to build estimators which minimize a penalized criterion. The method is illustrated by means of real data examples. Then, we give asymptotics results for these estimators.
引用
收藏
页码：49 / +
页数：3
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