Principled statistical methods for high-dimensional correlation networks

高维相关网络的原理统计方法

• 批准号: DP190103243
• 负责人: Prof Balakanapathy Rajaratnam
• 金额: $ 21.06万
• 依托单位: The University of Sydney
• 依托单位国家: 澳大利亚
• 项目类别: Discovery Projects
• 财政年份: 2019
• 资助国家: 澳大利亚
• 起止时间: 2019-04-15 至 2024-12-31
• 项目状态: 已结题
• 来源: https://dataportal.arc.gov.au/RGS/Web/Grants/DP190103243
• 关键词: Principled; statistical; methods; dimensional; correlation

This project aims to develop a novel and principled approach for building correlation networks. Correlation networks aim to identify the most significant associations present in modern massive datasets, and have numerous applications, ranging from the biomedical and environmental sciences to the social sciences. Nodes of such networks represent features, and edges represent associations, or the lack thereof. Current methods are not readily scalable to modern ultra-high dimensional settings, and do not account for uncertainty in the estimated associations. This project will develop a principled, highly scalable methodology for building such networks, which incorporates uncertainty quantification. Emphasis is placed on modern ultra-high dimensional settings in which differentiating a true correlation from a spurious one is a notoriously difficult task.

该项目旨在开发一种新颖且有原则的方法来构建相关网络。相关网络旨在识别现代海量数据集中存在的最重要的关联，并且具有从生物医学、环境科学到社会科学的众多应用。这种网络的节点代表特征，边代表关联或缺乏关联。当前的方法不容易扩展到现代超高维设置，并且没有考虑估计关联中的不确定性。该项目将开发一种原则性的、高度可扩展的方法来构建此类网络，其中包含不确定性量化。重点放在现代超高维设置上，其中区分真实相关性和虚假相关性是一项非常困难的任务。