ATD: Statistical methodology and algorithms for detection problems

ATD：检测问题的统计方法和算法

• 批准号: 1220311
• 负责人: Guenther Walther
• 金额: $ 47.16万
• 依托单位: Stanford University
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-09-01 至 2017-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1220311&HistoricalAwards=false
• 关键词: ATD; Statistical; methodology; algorithms; detection

The investigator will develop new statistical methodology and algorithms for the quick detection of the abrupt emergence of a signal which is observed by a sensor in a noisy data stream or by an array of sensors in multiple data streams. A particular emphasis will be on the construction of techniques for effectively combining the information from several sensors. Such techniques are essential when the signal is weak and observed by only a small fraction of the sensors. Part of the proposed new methodology is based on recent advances in the statistical theory of multiscale analysis. Theoretical investigations of these recent advances in the abstract Gaussian White Noise model suggest that clear improvements in detection power are possible for the problem of a quick detection of a change point, and the investigator plans to adapt these ideas for this problem, to investigate its theoretical performance, and to develop efficient algorithms for its implementation. The second main emphasis is to develop improved statistical methodology to combine the information from several data streams using a novel criterion based on the average likelihood ratio. In preliminary work the investigator has shown that this criterion results in superior detection power in a large-scale multiple testing context, and the investigator will develop corresponding methodology for the detection of a signal in multiple data streams.Change-point detection plays an important role in a range of problems such as the detection of radioactive and biochemical threats, environmental monitoring, or the detection of recurrent DNA copy number variants in multiple samples in high-thoughput genomics. Advances in detection methodology have thus a direct impact on important problems in national security and in high-thoughput genomics, e.g. in terms of a shorter time to the detection of chemical agents and biological threats. The investigator will adapt recent results in statistical theory to these detection problems. The theoretical results suggest that clear improvements in detection power are possible in these important problems.

研究人员将开发新的统计方法和算法，用于快速检测由噪声数据流中的传感器或多个数据流中的传感器阵列观察到的信号的突然出现。一个特别的重点将是构建有效结合来自几个传感器的信息的技术。当信号很弱，并且只有一小部分传感器可以观察到时，这种技术是必不可少的。拟议的新方法的一部分是基于多尺度分析统计理论的最新进展。对抽象高斯白噪声模型中这些最新进展的理论研究表明，对于快速检测变化点的问题，检测能力有可能明显提高，研究人员计划将这些想法应用于这个问题，研究其理论性能，并为其实现开发有效的算法。第二个主要重点是开发改进的统计方法，使用基于平均似然比的新标准将来自几个数据流的信息组合在一起。在前期工作中，研究人员已经证明，该标准在大规模多个测试环境中具有优越的检测能力，研究人员将开发相应的方法来检测多个数据流中的信号。变点检测在一系列问题中发挥着重要作用，如放射性和生化威胁的检测、环境监测或高吞吐量基因组学中多个样本中重复DNA拷贝数变异的检测。因此，检测方法的进步对国家安全和高产出基因组学中的重要问题具有直接影响，例如，在缩短检测化学制剂和生物威胁的时间方面。研究人员将使统计学理论中的最新结果适用于这些检测问题。理论结果表明，在这些重要问题上，探测能力的明显提高是可能的。