Robust Multivariate Analysis and Outlier Identification in Massive Data Sets

本研究涉及新的和改进的多变量位置和形状的鲁棒估计和异常值识别方法。海量数据集，特别是高维数据集，是研究的一个特殊目标。本研究聚焦于稳健多变量分析的理论、方法和计算问题，并将研究者先前的工作扩展到聚类分析。高维海量数据集中异常点和聚类的识别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中。它们的大小从数gb到tb不等，为分析带来了重大挑战。该项目旨在寻找揭示这些数据集中以前未知结构的方法。这些方法的计算密集型性质使这项研究直接与高性能计算和通信联邦战略领域有关，而环境科学数据的应用使其与环境和全球变化领域有关。该项目由国家高级计算基础设施伙伴关系支持并与之相关。

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.