ATD: Statistical methodology and algorithms for detection 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突然出现的信号。特别强调的是如何有效地组合来自多个传感器的信息。当信号很弱且只有一小部分传感器能观测到时，这种技术是必不可少的。部分提出的新方法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，检测能力的明显提高是可能的，研究者计划将这些想法应用于该问题，研究其理论性能，并为其实现开发有效的算法。第二个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结合起来。在初步工作中，研究者已经表明，该标准在大规模多重测试环境中具有优越的检测能力，并且研究者将开发相应的方法来检测多个数据流中的信号。在高通量基因组学中，变化点检测在一系列问题中发挥着重要作用，例如放射性和生化威胁的检测，环境监测或多样本中重复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通量基因组学中的重要问题产生了直接影响，例如，检测化学制剂和生物威胁的时间缩短了。研究者将利用统计理论的最新结果来解决这些检测问题。理论结果表明，在这些重要问题上，检测能力的明显提高是可能的。

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.