Collaborative Research: Asymptotic Statistical Inference for High-dimensional Time Series

合作研究：高维时间序列的渐近统计推断

• 批准号: 1916351
• 负责人: Wei Biao Wu
• 金额: $ 19万
• 依托单位: University of Chicago
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-08-01 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916351&HistoricalAwards=false
• 关键词: Collaborative; Research; Asymptotic; Statistical; Inference

The information era has witnessed an explosion in the collection of high dimensional time series data across a wide range of areas, including finance, signal processing, neuroscience, meteorology, seismology, among others. For low dimensional time series, there is a well-developed estimation and inference theory. Inference theory in the high dimensional setting is of fundamental importance and has wide applications, but has been rarely studied. Researchers face a number of challenges in solving real-world problems: (i) complex dynamics of data generating systems, (ii) temporal and cross-sectional dependencies, (iii) high dimensionality and (iv) non-Gaussian distributions. The goal of this project is to develop and advance inference theory for high dimensional time series data by concerning all the above characteristics. The project will provide training to graduate students and publicly avaialble statistical packages. This project involves developing a systematic asymptotic theory for estimation and inference for high dimensional time series, including parameter estimation, construction of simultaneous confidence intervals, prediction, model selection, Granger causality test, hypothesis testing, and spectral domain estimation. To this end, a new methodology for the estimation of parameters and second-order characteristics for high dimensional time series will be proposed. New tools and concentration inequalities for the asymptotic analysis of high-dimensional time series will be developed. To perform simultaneous inference and significance testing, the PIs will investigate the very deep Gaussian approximation problem and the high dimensional central limit theorems by taking both high dimensionality and temporal and cross-sectional dependencies into account.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.

信息时代目睹了在广泛领域的高维时间序列数据集合中的爆炸，包括金融，信号处理，神经科学，气象学，地震学等。对于低维时间序列，有一个发达的估计和推理理论。在高维环境中的推论理论至关重要，并且具有广泛的应用，但很少进行研究。研究人员在解决现实世界中的问题方面面临许多挑战：（i）数据生成系统的复杂动态，（ii）时间和横截面依赖性，（iii）高维度和（iv）非高斯分布。该项目的目的是通过上述所有特征来开发和推进高维时间序列数据的推理理论。该项目将为研究生和公开宣传的统计包提供培训。该项目涉及开发一种系统的渐近理论，以估算高维时间序列，包括参数估计，同时置信区间的构建，预测，模型选择，Granger因果关系测试，假设检验和光谱域估计。为此，将提出一种用于估计参数和二阶特征的新方法。将开发用于高维时间序列的渐近分析的新工具和集中不平等。为了同时进行推断和显着性测试，PI将通过考虑高维度，时间和跨截面依赖性来调查非常深的高斯近似问题和高维中心限制定理，这是NSF的立法任务。