Robust Multivariate Analysis and Outlier Identification in Massive Data Sets

Rocke9870172这项研究涉及新的和改进的方法，用于稳健估计多元位置和形状以及识别异常值。 海量数据集，尤其是高维数据集，构成了研究的特定目标。 该研究重点关注稳健多元分析中的理论、方法和计算问题，并将研究者之前的工作扩展到聚类分析。高维和海量数据集中的异常值和数据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、地球物理学以及许多其他科学领域。 它们的大小从数 GB 到 TB 不等，给分析带来了巨大的挑战。 该项目旨在寻找揭示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将这项研究直接与高性能计算和通信联邦战略领域联系起来，而环境科学数据的应用则将其与环境和全球变化领域联系起来。 该项目得到国家高级计算基础设施合作伙伴的支持并与之相关。

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.