Principled statistical methods for high-dimensional correlation networks

本项目旨在开发一种新的、原则性的方法来构建相关网络。关联网络旨在识别现代海量数据集中存在的最重要的关联，并有许多应用，从生物医学和环境科学到社会科学。这种网络的节点表示特征，而边表示关联或缺乏关联。目前的方法不容易扩展到现代超高维设置，并且不考虑估计关联中的不确定性。该项目将开发一个原则性的、高度可扩展的方法来构建这样的网络，其中包括不确定性量化。重点放在现代超高维环境中，区分真实的相关性和虚假的相关性是一项众所周知的困难任务。

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.