Collaborative Research: Non-Parametric Inference of Temporal Data

合作研究：时态数据的非参数推理

• 批准号: 2311249
• 负责人: Wei Biao Wu
• 金额: $ 25.29万
• 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: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2311249&HistoricalAwards=false
• 关键词: Collaborative; Research; Non; Parametric; Inference

This project is driven by the need to address inquiries in diverse fields, including environmental sciences, epidemiology, and economics among others. The study of extreme weather events, such as tropical storms, requires meteorologists to determine whether more potent tropical storms occur more frequently than mid or low-level tropical storms over time. Epidemiologists studying the transmissibility and severity of COVID-19 utilize clinical laboratory data to evaluate the pattern of the trends. In investigating sea pollution levels, earth scientists gather data on mercury concentration in animals to determine whether there has been a rising trend in mercury concentration over the years. The primary objective of this research project is to enhance the methods used to tackle these questions and effectively communicate findings to the scientific community and the public. More informed decisions can be made based on the findings. This project also involves training and mentoring graduate students through their active involvement in the research. The research team aims to develop innovative statistical methods to study temporally observed or time-indexed multi-sample data, which consist of measurements of different subjects made at different time points. Such data do not fall within the conventional univariate or high-dimensional time series since measurements at different time points may not have an inherent connection. The investigators and collaborators will develop a systematic asymptotic theory to address this challenge to estimate and infer temporally observed multi-sample data. They will establish consistency, asymptotic normality, and an extremal distribution theory for various associated statistics and study simultaneous confidence bands and change points analysis.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.

该项目的驱动是由于需要解决各种领域的询问，包括环境科学，流行病学和经济学等。对热带风暴等极端天气事件的研究要求气象学家确定随着时间的推移，更多有效的热带风暴比中期或低水平的热带风暴更频繁。研究Covid-19的传播性和严重性的流行病学家利用临床实验室数据来评估趋势的模式。在调查海洋污染水平时，地球科学家收集了有关动物汞浓度的数据，以确定多年来汞浓度的趋势趋势上升。该研究项目的主要目的是增强用于解决这些问题的方法，并有效地向科学界和公众传达发现。可以根据发现做出更多明智的决定。该项目还涉及通过积极参与研究的培训和指导研究生。研究小组旨在开发创新的统计方法，以研究时间分配或时间指数的多样本数据，这些数据包括对不同时间点上不同受试者的测量。此类数据不属于常规单变量或高维时间序列，因为在不同时间点的测量值可能没有固有的连接。研究人员和合作者将开发一种系统的渐近理论，以应对这一挑战，以估算并推断出时间观察到的多样本数据。他们将建立一致性，渐近态性，以及针对各种相关统计数据的极端分布理论，并研究同时置信频段和变更点分析。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的智力优点和更广泛的影响来通过评估来支持的。