"Functional and High-dimensional Data Analysis: Regularization, Representation and Regression"

现代科学实验中收集到的大量复杂数据，由于其高维和复杂的结构，对传统的统计方法提出了巨大的挑战。功能数据分析(FDA)是一个以潜在或观察到的随机过程的实现为模型单元的很有前途的领域，其应用广泛地存在于许多领域，如纵向研究、微阵列数据、脑图像、互联网调查等。功能数据建模的一个基本问题源于“维度诅咒”，因为功能对象在概念上被框架为无限维过程。这自然与最近革命性的高维模型选择和正规化领域有关。

Large complex data collected in modern scientific experiments impose tremendous challenges for traditional statistical methods due to high-dimensional and complicated structures. Functional data analysis (FDA) has emerged as a promising field that employs realizations of latent or observed stochastic processes as model units, whose applications are commonly found in many fields, such as longitudinal studies, microarray data, brain images, internet investigations, etc. A fundamental issue of modeling functional data arises from the "curse of dimensionality", as functional objects are conceptually framed as infinite-dimensional processes. This naturally relates to the recently revolutionized area of high-dimensional model selection and regularization.