Networks for multivariate time series

多元时间序列网络

• 批准号: RGPIN-2018-06638
• 负责人: Cribben, Ivor
• 金额: $ 1.31万
• 依托单位: University of Alberta
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=676910
• 关键词: Networks; multivariate; time; series

This program focuses on the development of novel statistical methods for high dimensional time series data with a particular emphasis on the application of these methods to data from neuroimaging. The analysis of time series data has been of interest to statisticians for many decades. Traditionally, researchers examined problems relating to one time series and eventually to multiple time series. In this program, we concentrate on specific problems in high dimensional time series analysis. Firstly, we consider creating original statistical methodology for classes of processes that can experience changes in their generating mechanism over the time course of observation. These processes are important as they allow for the modelling of the evolution of an observable quantity, and enable the quantification of this evolution explicitly. Secondly, we propose new methods for testing networks between groups of subject specific time series and also for estimating and testing longitudinal networks from groups of subject specific time series. Thirdly, we introduce novel methods that classify networks from subject specific multivariate time series into groups that correspond to discrete events (such as presence of disease). Finally, we are interested in developing statistical methodology that evaluate how the network structure relate to a set of behaviors. ******The work proposed in this program is fundamental research in statistics, but it also directly impacts other areas such as engineering, economics and the natural sciences. Statistics also has an additional impact on society through collaborations and users of developed technologies, such as computer software. In short, statistics is how we make sense of information, and the world around us. The methods created in this program have a broad range of applicability due to the ubiquity of high dimensional time series in many applications. This increases the potential for impact. A number of the proposed collaborations in this project will ensure that the methodological developments will connect to end-users and that the results will be of direct practical utility and of importance to society. In particular, as the people of Canada and other countries across the world live longer lives, the number of brain disorders such as Alzheimer's disease is certain to rise dramatically. Resting state functional magnetic resonance imaging (fMRI) is a popular tool to study neurological disorders and the development of nonstationary statistical methods will significantly contribute to the understanding of these disorders and the possible identification of biomarkers. In addition, testing group differences in baseline networks and network aging effects for longitudinal data is very important in order to understand brain function and to identify possible biomarkers for disease. This research will eventually impact clinical practice through my collaborations.

该计划的重点是开发新的统计方法，为高维时间序列数据，特别强调这些方法的应用，从神经影像学数据。几十年来，时间序列数据的分析一直是统计学家的兴趣所在。传统上，研究人员研究与一个时间序列相关的问题，并最终研究多个时间序列。在这个程序中，我们集中在高维时间序列分析的具体问题。 首先，我们考虑创建原始的统计方法的类的过程中，可以经历的变化，其生成机制在观察的时间过程。这些过程很重要，因为它们允许对可观察量的演变进行建模，并能够明确量化这种演变。其次，我们提出了新的方法来测试网络组之间的主题特定的时间序列，也估计和测试纵向网络组的主题特定的时间序列。第三，我们引入了新的方法，将网络从特定于主题的多变量时间序列分类为与离散事件（如疾病的存在）相对应的组。最后，我们感兴趣的是开发统计方法，评估网络结构如何与一组行为相关。* 该计划中提出的工作是统计学的基础研究，但它也直接影响其他领域，如工程，经济和自然科学。统计还通过协作和使用计算机软件等发达技术对社会产生额外影响。简而言之，统计数据是我们理解信息和周围世界的方式。由于高维时间序列在许多应用中的普遍性，本程序中创建的方法具有广泛的适用性。这增加了影响的可能性。该项目中的一些拟议合作将确保方法的发展与最终用户联系起来，并确保其结果具有直接的实际效用和对社会的重要性。特别是，随着加拿大和世界其他国家人民的寿命延长，阿尔茨海默病等脑部疾病的数量肯定会急剧增加。静息态功能磁共振成像（fMRI）是一种流行的工具，研究神经系统疾病和非平稳统计方法的发展将显着有助于这些疾病的理解和可能的生物标志物的识别。此外，测试基线网络的组间差异和纵向数据的网络老化效应对于了解大脑功能和识别疾病的可能生物标志物非常重要。这项研究最终将通过我的合作影响临床实践。