Variable selection in functional regression models: A review

被引：18

作者：

Aneiros, German ^{[1
,2
]}

Novo, Silvia ^{[1
]}

Vieu, Philippe ^{[3
]}

机构：

[1] Univ A Coruna, Dept Matemat, CITIC, MODES, Coruna, Spain

[2] ITMATI, ACoruna, Spain

[3] Univ Paul Sabatier, Inst Mathemat, Toulouse, France

来源：

JOURNAL OF MULTIVARIATE ANALYSIS | 2022年 / 188卷

关键词：

Functional Data Analysis; Regression; Variable selection; NONCONCAVE PENALIZED LIKELIHOOD; SHRINKAGE ESTIMATION; DIMENSION REDUCTION; LINEAR-MODEL; LASSO;

D O I：

10.1016/j.jmva.2021.104871

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：

Despite of various similar features, Functional Data Analysis and High-Dimensional Data Analysis are two major fields in Statistics that grew up recently almost independently one from each other. The aim of this paper is to propose a survey on methodological advances for variable selection in functional regression, which is typically a question for which both functional and multivariate ideas are crossing. More than a simple survey, this paper aims to promote even more new links between both areas. (C) 2021 Elsevier Inc. All rights reserved.

引用

页数：13

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