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.
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页数：13
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