On dimension reduction models for functional data

被引：29
|
作者：
Vieu, Philippe [1 ]
机构：
[1] Univ Paul Sabatier, UMR5219, Inst Math Toulouse, F-31062 Toulouse 9, France
来源：
STATISTICS & PROBABILITY LETTERS | 2018年 / 136卷
关键词：
Functional data; Dimension reduction; Semi-parametrics; Sparse regression; VARIABLE SELECTION; LINEAR-REGRESSION; GAUSSIAN-PROCESSES; SINGLE; CLASSIFICATION; ESTIMATORS;
D O I：
10.1016/j.spl.2018.02.032
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：
This contribution is part of the recent links between Functional Data and Big Data communities. A selected survey highlights how earlier ideas in high dimensional problems can be adapted in functional setting. (C) 2018 Elsevier B.V. All rights reserved.
引用
收藏
页码：134 / 138
页数：5
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