High-dimensional structured regression

高维结构化回归

• 批准号: 1208857
• 负责人: Jonathan Taylor
• 金额: $ 29.99万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-08-01 至 2016-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1208857&HistoricalAwards=false
• 关键词: dimensional; structured; regression

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.

这个项目的重点是结构化的高维回归与长期结构的意思是从其他方法，如l1最小化方法区分的方法。一般来说，这种结构是先验假设的，并在找到回归问题的可解释解决方案的基础上选择。在这个项目中，结构通常是指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。第二，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能够提高神经科学家基于fMRI或其他时空结构数据预测行为的能力。