PLS classification of functional data

被引：100
|
作者：
Preda, Cristian
Saporta, Gilbert
Leveder, Caroline
机构：
[1] Univ Lille 2, Fac Med, Dept Stat, CERIM, F-59045 Lille, France
[2] Conservatoire Natl Arts & Metiers, CEDRIC, Chair Stat Appl, F-75141 Paris 03, France
[3] Danone Vitapole, F-91767 Palaiseau, France
来源：
COMPUTATIONAL STATISTICS | 2007年 / 22卷 / 02期
关键词：
PLS regression; functional data; linear discriminant analysis;
D O I：
10.1007/s00180-007-0041-4
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：
Partial least squares (PLS) approach is proposed for linear discriminant analysis (LDA) when predictors are data of functional type (curves). Based on the equivalence between LDA and the multiple linear regression (binary response) and LDA and the canonical correlation analysis (more than two groups), the PLS regression on functional data is used to estimate the discriminant coefficient functions. A simulation study as well as an application to kneading data compare the PLS model results with those given by other methods.
引用
收藏
页码：223 / 235
页数：13
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