High-dimensional structured regression

这个项目的重点是结构化高维回归，其术语结构旨在将这些方法与其他方法，如L1最小化方法区分开来。一般来说，这种结构是先验假设的，是根据找到回归问题的可解释解决方案来选择的。在本项目中，结构通常指的是在神经成像或天文数据等应用领域中发现的空间结构。该项目有两个主要目标。首先开发可扩展的、灵活的算法和软件实现，以适应这种结构化模型。其次，了解这些模型的统计性能以及用于拟合这些模型的算法。本项目提出的研究结果将使神经科学领域的研究人员提高神经科学家基于功能磁共振成像或其他时空结构化数据预测行为的能力。

This project is focused on structured high dimensional regression with the term structure meant to distinguish the methods from other methods such as l1 minimization methods. Generally speaking, this structure is assumed a priori and is chosen on the basis of finding an interpretable solution to a regression problem. In this project, structure often refers to spatial structure found in areas of application such as neuroimaging or astronomical data. The project has two principal goals. First to develop scalable, flexible algorithms and software implementations for fitting such structured models. Secondly, to understand the statistical performance of such models as well as the algorithms used to fit such models.The results of the research proposed in this project will allow researcher in the field of neuroscience to improve neuroscientists' ability to predict behavior based on fMRI or other spatio-temporally structured data.