Change-point detection - theory and applications

变点是指观测或数据服从两个不同模型的位置或时间：之前和之后。变点问题的统计学研究可以追溯到Page（1955），特别是自20世纪80年代以来蓬勃发展。这些研究已在广泛的领域得到应用，包括质量控制、金融、计量学、医学、遗传学和地理学。这项研究计划的一个重要组成部分是开发快速准确的变点检测方法。 通过对数据序列进行适当的分段，将多变点问题转化为变量选择问题，是解决多变点问题的一种有效方法。因此，这两个问题之间的联系，并提出了一个框架，通过以下两个关键步骤来解决多个变化点检测：（1）应用一致的变量选择方法的最新进展，以检测变化点;（2）采用一个改进的过程，以提高变化点估计的准确性。 本研究计划的第一部分是继续研究大型复杂数据集的变量选择方法，开发使用统计计算软件R实现的快速有效的算法，并建立统计理论来支持开发的算法。 其次，为了精确地细化变点的位置，将以加权双部分和的形式对检验统计量的分布函数应用鞍点近似。近似是非常准确的，不需要使用模拟或近似使用布朗桥。 第三，基于图的加权双部分和统计测试变点将提出，利用相似图和最小生成树表示。这种方法的动机是研究高通量数据，其中每个观察的维度可以大于序列的长度。 该提案寻求资金，以进行系统的研究变点检测和变量选择之间的关系，开发准确的鞍点近似，并探讨在许多领域的应用。本研究计划中开发的基本统计理论将显著提高我们对多个变点检测的理解，并为在许多科学领域收集的高维数据开发新的测试统计量指明方向，包括高通量遗传学，生物计量学，地震勘探和金融。

A change point refers to a location or time at which observations or data obey two different models: before and after. Statistical studies of change-point problems date back to Page (1955) and have flourished especially since the 1980s. These studies have found applications in a wide range of areas, including quality control, finance, environmetrics, medicine, genetics and geography. An essential part of this proposed research program is to develop fast and accurate methods for change-point detection. A promising method for tackling the multiple change-point problem is to convert it to the variable selection problem by segmenting the data sequence properly. Thus, a connection between the two problems is established, and a framework is proposed to tackle multiple change point detection via the following two key steps: (1) apply the recent advances in consistent variable selection methods to detect change points; (2) employ a refined procedure to improve the accuracy of change-point estimation. The first part of this proposed research program is to continue the investigation of variable selection methods in large and complex data sets, to develop fast and efficient algorithms implemented using the statistical computing software R, and to establish statistical theory to support the developed algorithms. Second, in order to accurately refine the location of the change-point, a saddlepoint approximation will be applied to the distribution function of the test statistic, in the form of a weighted double partial sum. The approximation is very accurate and does not require the use of simulations or approximation using the Brownian bridge. Third, graph-based weighted double partial sum statistics for testing change-points will be proposed, utilizing similarity graphs and minimum spanning tree representations. This approach is motivated by the study of high-throughput data, where the dimension of each observation can be larger than the length of the sequence. This proposal seeks funding to carry out a systematic study of relationships between change-point detection and variable selection, to develop accurate saddlepoint approximations, and to explore applications in many fields. The fundamental statistical theory developed in this research program will improve our understanding of multiple change-point detection significantly and point the way towards developing new test statistics for high-dimensional data collected in many areas of science, including high-throughput genetics, environmetrics, seismic exploration, and finance.