Robust Multivariate Analysis and Outlier Identification in Massive Data Sets

这项研究涉及新的和改进的方法，用于多变量位置和形状的稳健估计以及异常值的识别。海量数据集，特别是高维数据集，构成了这项研究的特定目标。研究集中在稳健多元分析的理论、方法和计算问题上，并将研究人员以往的工作扩展到聚类分析中，识别高维和海量数据集中的离群点和聚类是一个重要而困难的问题。这些数据集出现在卫星图像、人类基因组计划、X射线天文学、地球物理学和许多其他科学领域。它们的大小从数GB到TB不等，给分析带来了巨大的挑战。该项目旨在发现这些数据集中以前未知的结构的方法。这些方法的计算密集型性质直接涉及到高性能计算和通信联邦战略领域，而对环境科学数据的应用则涉及到环境和全球变化领域。该项目得到了先进计算基础设施国家伙伴关系的支持和关联。

Rocke9870172This research involves new and improved methods for robust estimation of multivariate location and shape and identification of outliers. Massive data sets, especially in high dimension, form a particular target of the research. The research focuses on theoretical, methodological, and computational questions in robust multivariate analysis, and also extends the investigators previous work into cluster analysis.Identification of outliers and clusters of data in high dimension and in massive data sets is an important and difficult problem. These data sets occur in satellite imagery, in the human genome project, in x-ray astronomy, in geophysics, and in many other areas of science. They range in size from multiple gigabytes to terabytes, and present significant challenges for analysis. This project is aimed at methods for uncovering previously unknown structure in these data sets. The computationally intensive nature of the methods relates this research directly to the High Performance Computing and Communication Federal Strategic Area, and the applications to environmental science data relates it to the Environment and Global Change area. The project is supported by and associated with the National Partnership for Advanced Computational Infrastructure.