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Statistical Inference for High-Dimensional Time Series

高维时间序列的统计推断

基本信息

批准号：
1807023
负责人：
Xiaofeng Shao
金额：
$ 12万
依托单位：
University of Illinois at Urbana-Champaign
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2018
资助国家：
美国
起止时间：
2018-08-15 至 2022-07-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1807023&HistoricalAwards=false
关键词：
Statistical Inference Dimensional Time Series

项目摘要

Due to the rapid development of information technologies and their applications in many scientific fields, high dimensional time series (HDTS) are routinely collected nowadays. The methods and theory developed for the inference of low and fixed dimensional time series may not be applicable when the dimension is comparable to or exceeds time series length, and there is an urgent need to develop new statistical methods that can accommodate both high dimensionality and temporal dependence. Statistical inference for HDTS is fundamentally important and has many applications in disciplines ranging from climate science to medical imaging and finance, among others.This project aims to develop innovative theory and methodologies to address several important inference problems in the analysis of HDTS. The research is built on the self-normalized approach, which has found great success in dealing with low dimensional problems. Its advance to the high dimensional context is challenging both methodologically and theoretically, and it requires a new methodological formulation and new theory. This project covers the inference of the mean, covariance matrix, and auto-covariance matrix for HDTS, and the tests developed can be used to detect change points, certain structure of the covariance matrix and target dense alternative. On the theoretical front, the weak convergence of sequential U-statistic based process will be investigated and is of independent interest.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

随着信息技术的快速发展及其在许多科学领域的应用，高维时间序列（HDTS）已成为一种常规的数据采集方法。对于低维和固定维时间序列的推断方法和理论，当维度与时间序列长度相当或超过时间序列长度时，可能不适用，迫切需要开发既能适应高维又能适应时间依赖性的新的统计方法。HDTS的统计推断至关重要，在从气候科学到医学成像和金融等学科中有许多应用。本项目旨在发展创新的理论和方法，以解决HDTS分析中的几个重要推理问题。该研究建立在自归一化方法的基础上，该方法在处理低维问题方面取得了巨大成功。它向高维语境的推进在方法论和理论上都具有挑战性，需要新的方法论制定和新的理论。本项目涵盖了HDTS的均值、协方差矩阵和自协方差矩阵的推理，所开发的测试可用于检测变点、协方差矩阵的某些结构和目标密集替代。在理论方面，基于顺序u统计量的过程的弱收敛性将被研究，这是一个独立的兴趣。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。