Mathematical Sciences: Location of Outliers in Structured Data

主要研究者将研究检测 结构化数据中的异常值。 离群值是指 似乎与大量的观察结果不同。 已知的是 许多参数估计程序不能产生好的结果 当存在异常值时。 有鉴于此，一种方法 处理异常值是最初检测它们的存在。 的 研究人员将继续研究这一检测问题， 关于元素集程序，其中涉及拟合到 数据许多模型，每个模型都精确拟合最小尺寸的子集 原始数据。 综合这些信息 以适当的方式，人们可以识别多个元素集合， 异常值可靠，并且在单次通过中。

The principal investigator will study the detection of outliers in structured data. Outliers are observations which appear different from the bulk of observations. It is known that many parameter estimation procedures fail to produce good results when outliers are present. In light of this, one method of handling outliers is to initially detect their presence. The investigator will continue to study this detection problem based on the elemental sets procedure, which involves fitting to the data many models, each an exact fit to a minimally-sized subset of the original data. Combining the information from these elemental sets in suitable ways, one may identify the multiple outliers reliably, and in a single pass.