Statistical Learning

统计学习

• 批准号: CRC-2019-00246
• 负责人: Kong, Linglong
• 金额: $ 8.74万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Canada Research Chairs
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=730706
• 关键词: Statistical; Learning

Neuroimaging data analysis (NDA) has recently emerged as part of the rapidly evolving field of big and complex data analysis. Neuroimaging data, for example, diffusion tensor imaging (DTI) and magnetic resonance imaging (MRI), could be treated as functional data while having its own features. Therefore, its statistical analysis inherits some methods from functional data analysis (FDA) and also poses new challenges due to its complex structure. To address those challenges, we develop novel statistical methods in FDA and NDA to explore its complex and correlated structures and integrate data from other resources such as clinical data and genetics data.

神经影像数据分析（NDA）最近已经成为快速发展的大数据和复杂数据分析领域的一部分。神经影像学数据，如弥散张量成像（DTI）和磁共振成像（MRI），可以被视为功能数据，同时有自己的特点。因此，它的统计分析继承了功能数据分析（FDA）的一些方法，也提出了新的挑战，由于其复杂的结构。为了应对这些挑战，我们在FDA和NDA中开发了新的统计方法，以探索其复杂和相关的结构，并整合来自其他资源的数据，如临床数据和遗传学数据。