Recent years have seen an explosion of activity in the field of functional data analysis (FDA), in which curves, spectra, images, etc. are considered as basic functional data units. A central problem in FDA is how to fit regression models with scalar responses and functional data points as predictors. We review some of the main approaches to this problem, categorizing the basic model types as linear, nonlinear and nonparametric. We discuss publicly available software packages, and illustrate some of the procedures by application to a functional magnetic resonance imaging dataset.

中文翻译：

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函数标量回归方法

近年来，功能数据分析（FDA）领域的活动呈爆炸式增长，其中曲线、光谱、图像等被视为基本功能数据单元。FDA 的一个核心问题是如何用标量响应和功能数据点作为预测因子来拟合回归模型。我们回顾了解决这个问题的一些主要方法，将基本模型类型分为线性、非线性和非参数。我们讨论公开可用的软件包，并通过应用于功能磁共振成像数据集来说明一些过程。