Mathematical Sciences: Processing Massive Noisy Data with Hidden Structure

提案：DMS 95-04414 主要研究者： 范建青- 1989年，斯蒂芬·马龙- 1982年 机构名称： 北卡罗来纳大学查佩尔山 职务名称： 利用隐结构处理海量噪声数据 摘要： 这项研究涉及到一些相互关联的统计问题，从处理图像到发现信息结构 从海量数据，到分析生存数据，评估商业广告的成功，以及了解动物的活动范围。 与传统的方法相比，该方法没有任何限制性的形式，并且足够灵活，可以检测精细结构。 该项目涵盖了广泛的统计方法的发展和基础研究。 新的发展提高了 统计工具，并可能导致显着改善现有的方法，从嘈杂的数据中提取信息。具体目标是开发新的统计方法，并研究其基本属性，用于处理高维数据，非参数置信区间，模式检测和信号处理中的灵活统计建模。 许多科学学科在某种程度上依赖于从噪声数据中提取结构信息。 从处理嘈杂的图像和评估商业营销，到分析生存数据， 预测经济气候的背景是不同的，但它们有一个共同的问题，即通过处理嘈杂的信号得出结论。 这类问题可以抽象为一个统计函数估计 问题，并可以通过该项目中的各种技术进行分析。 本研究的目的是开发和评估灵活的统计建模技术。 这些技术可以应用于联邦战略领域，例如通过处理大量收集的数据来监测环境和全球变化，其中传统方法很难检测到信息结构， 以及为民用基础设施的经济和商业活动建立统计模型。调查人员将利用现代计算设施，并利用统计知识，以避免不必要的数据挖掘，从而大大减少数据处理时间。 因此，本研究中获得的知识也将是有用的 在联邦战略领域的高性能计算。

Proposal: DMS 95-04414 PI(s): Jianqing Fan - 1989, Stephen Marron - 1982 Institution: U of NC Chapel Hill Title: Processing Massive Noisy Data with Hidden Structure Abstract: This research involves a few inter-related statistical problems ranging from processing images and finding informative structure from massive data, to analyzing survival data, evaluating the success of a business advertisement and understanding the home range of animals. In contrast with traditional approaches, the methods do not assume any restrictive form, and are flexible enough to detect fine structures. This project covers a wide array of statistics methodological developments and foundational research. The new developments enhance the availability of statistical tools, and can lead to significant improvements on existing methods for extracting information from noisy data. Specific objectives are develop new statistical methodologies and investigate their foundational properties for flexible statistical modeling in processing high-dimensional data, nonparametric confidence intervals, mode detection and signal processing. Many scientific disciplines depend in some way on extracting structural information from noisy data. Fields ranging from processing noisy images and evaluating business marketing, to analyzing survival data and forecasting economic climates, which are universes apart in their backgrounds, nevertheless have the common problem of drawing conclusions via processing noisy signals. Such problems may be abstracted as a statistical function estimation problem and can be analyzed by various techniques in this project. The objective of this research is to develop and evaluate flexible statistical modeling techniques. These techniques can be applied in the Federal Strategic Areas such as monitoring environmental and global changes via processing massive collected data where informative structures can hardly be detected by traditional approache s, and building statistical modeling for economic and business activities in the civil infrastructure. The investigators will take advantage of modern computing facilities, and use statistical knowledge to avoid unnecessary data mining and hence reduce significantly data processing time. Thus, the knowledge gained in this study will also be useful in high performance computing of a federal strategic area.