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

页码：134 / 138

页数：5

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