Statistical Modelling and Inference for Next-Generation Functional Data

下一代功能数据的统计建模和推理

• 批准号: 1916204
• 负责人: Lily Wang
• 金额: $ 12.5万
• 依托单位: Iowa State University
• 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 2021-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1916204&HistoricalAwards=false
• 关键词: Statistical; Modelling; Inference; Next; Generation

With the rapid growth of modern technology, many large-scale imaging studies have been or are being conducted to collect massive datasets with large volumes of imaging data, thus boosting the investigation of "next-generation functional data". These enormous collections of imaging data contain interesting information and valuable knowledge, which has raised the demand for further advancement in functional data analytic approaches. Although functional data analysis (FDA) has gained widespread popularity in recent years, enhancing the capability of next-generation FDA remains a long-standing challenge. This research targets integrating state-of-the-art statistical modeling devices with modern computational and inferential techniques to develop a set of flexible and intelligent statistical tools to enable learning and discovery from next-generation functional data. The efficacy of the tools developed in this research will be tested by neuroimaging studies. The proposed methods and theory are also applicable to a broader range of fields that require modeling and analysis of images and other complex data types collected over space and/or time, such as geography, environmental science and remote sensing studies. The graduate student support will be used for day-to-day research activities, including parts of the theory/methodology developments and data analysis. This research will enrich the methods for dealing with functional data observed from complex data objects (high-dimensional, correlated images or shapes), which commonly arise in imaging studies, such as, health/medical imaging or remote sensing imaging. The PI aims to address some challenging research problems in analyzing next-generation functional data by: (1) innovating a statistically sound framework to extract useful information from large-scale longitudinal imaging studies; (2) developing flexible and intelligent statistical models to delineate the association between massive imaging data and covariates of interest and to characterize and visualize the spatial variability of the imaging data; and (3) developing efficient, scalable algorithms with high-performance statistical software packages to meet the challenges posed by dynamic imaging studies. In particular, the proposed research involves four projects. Project 1 provides a unifying approach to characterize the varying association between imaging responses with a set of explanatory variables. Project 2 focuses on the interface between high-dimensional and next-generation functional data to address several fundamental bottlenecks in large-scale imaging genetics studies. Projects 3 and 4 deal with longitudinal/dynamic imaging studies, and a comprehensive functional regression framework to analyze repeated functional responses from these studies will be developed.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.

随着现代科学技术的飞速发展，许多大规模的影像学研究已经或正在进行，以收集具有大量影像数据的海量数据集，从而推动了“下一代功能数据”的研究。这些庞大的成像数据集合包含有趣的信息和有价值的知识，这就提出了进一步推进功能数据分析方法的需求。虽然功能数据分析（FDA）近年来得到了广泛的普及，但提高下一代FDA的能力仍然是一个长期的挑战。该研究的目标是将最先进的统计建模设备与现代计算和推理技术相结合，开发一套灵活和智能的统计工具，以便从下一代功能数据中进行学习和发现。本研究中开发的工具的有效性将通过神经影像学研究进行测试。所提出的方法和理论也适用于更广泛的领域，这些领域需要对在空间和/或时间上收集的图像和其他复杂数据类型进行建模和分析，例如地理，环境科学和遥感研究。研究生支持将用于日常研究活动，包括理论/方法开发和数据分析的部分。这项研究将丰富的方法来处理从复杂的数据对象（高维，相关的图像或形状），这通常出现在成像研究，如，健康/医学成像或遥感成像观察到的功能数据。PI旨在通过以下方式解决分析下一代功能数据中的一些具有挑战性的研究问题：（1）创新统计学合理的框架，以从大规模纵向成像研究中提取有用的信息;（2）开发灵活和智能的统计模型，以描述大量成像数据与感兴趣的协变量之间的关联，并表征和可视化成像数据的空间变异性;以及（3）开发具有高性能统计软件包的高效、可扩展算法，以应对动态成像研究带来的挑战。具体而言，拟议的研究涉及四个项目。项目1提供了一种统一的方法来表征成像响应与一组解释变量之间的不同关联。项目2的重点是高维和下一代功能数据之间的接口，以解决大规模成像遗传学研究中的几个基本瓶颈。项目3和项目4涉及纵向/动态成像研究，并将开发一个综合功能回归框架来分析这些研究的重复功能反应。该奖项反映了NSF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。

项目成果

Spatially Varying Coefficient Models with Sign Preservation of the Coefficient Functions

• DOI: 10.1007/s13253-021-00443-5
• 发表时间: 2021-02
• 期刊: Journal of Agricultural, Biological and Environmental Statistics
• 作者: Myungjin Kim;Li Wang;Yu-ying Zhou

Sparse Learning and Structure Identification for Ultrahigh-Dimensional Image-on-Scalar Regression

• DOI: 10.1080/01621459.2020.1753523
• 发表时间: 2020-05-26
• 期刊: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• 影响因子: 3.7
• 作者: Li, Xinyi;Wang, Li;Wang, Huixia Judy
• 通讯作者: Wang, Huixia Judy

Modeling and Forecasting COVID-19

COVID-19 建模和预测

• DOI: 10.1090/noti2263
• 发表时间: 2021
• 期刊: Notices of the American Mathematical Society
• 作者: Wang, Lily;Wang, Guannan;Li, Xinyi;Yu, Shan;Kim, Myungjin;Wang, Yueying;Gu, Zhiling;Gao, Lei
• 通讯作者: Gao, Lei

Simultaneous confidence corridors for mean functions in functional data analysis of imaging data

• DOI: 10.1111/biom.13156
• 发表时间: 2019-11
• 期刊: Biometrics
• 影响因子: 1.9
• 作者: Yueying Wang;Guannan Wang;Li Wang;R. Ogden

>Efficient Estimation of Partially Linear Models for Data on Complicated Domains by Bivariate Penalized Splines over Triangulations

>通过三角剖分上的二元惩罚样条有效估计复杂域数据的部分线性模型

• DOI: 10.5705/ss.202017.0243
• 发表时间: 2020
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Wang, Lily;Wang, Guannan;Lai, Ming-Jun;Gao, Lei
• 通讯作者: Gao, Lei