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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=714373
• 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.

本课程的重点是开发高维时间序列数据的新统计方法，特别强调这些方法在神经成像数据中的应用。几十年来，时间序列数据的分析一直是统计学家感兴趣的问题。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。在这个节目中，我们集中讨论高维时间序列分析中的具体问题。首先，我们考虑为能够在观察过程的时间过程中经历其生成机制变化的过程类别创建原始的统计方法。这些过程很重要，因为它们允许对可观测量的演变进行建模，并能够明确地量化这种演变。其次，我们提出了测试特定主题时间序列组之间网络的新方法，以及从特定主题时间序列组估计和测试纵向网络的新方法。第三，我们引入了新的方法，将来自特定主题的多变量时间序列的网络分类到对应于离散事件(如疾病的存在)的组中。最后，我们对开发评估网络结构如何与一组行为相关的统计方法感兴趣。 这项计划提出的工作是统计学的基础研究，但它也直接影响到其他领域，如工程学、经济学和自然科学。统计还通过计算机软件等发达技术的协作和用户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我们周围世界的方式。由于高维时间序列在许多应用中无处不在，本程序中创建的方法具有广泛的适用性。这增加了产生影响的可能性。该项目中的一些拟议合作将确保方法的发展将与最终用户联系起来，并确保其结果将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的人们寿命延长，阿尔茨海默氏症等大脑疾病的数量肯定会急剧上升。静息状态功能磁共振成像(FMRI)是研究神经系统疾病的常用工具，非平稳统计方法的发展将有助于了解这些疾病和可能的生物标志物的识别。此外，为了了解大脑功能和确定疾病的可能生物标志物，测试组内基线网络的差异和纵向数据的网络老化效应非常重要。这项研究最终将通过我的合作影响临床实践。