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.

翻译：尽管具有诸多相似特征，函数型数据分析与高维数据分析是统计学中近年来几乎独立发展的两大领域。本文旨在综述函数型回归中变量选择的方法论进展——这恰恰是一个函数型思想与多元思想相互交叉的典型问题。本文不止于简单的综述，更旨在促进两个领域之间建立新的联系。