Statistical Modeling and Inference for Network Data in Modern Applications

现代应用中网络数据的统计建模和推理

基本信息

批准号：
2326893
负责人：
Emma Jingfei Zhang
金额：
$ 19.25万
依托单位：
Emory University
依托单位国家：
美国
项目类别：
Continuing Grant
财政年份：
2023
资助国家：
美国
起止时间：
2023-01-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2326893&HistoricalAwards=false
关键词：
Statistical Modeling Inference Network Data

项目摘要

In modern data science, networks have emerged as one of the most important and ubiquitous types of non-traditional data. Recently, data sets with a large number of independent network-valued samples have become increasingly available. In such data sets, a network serves as the basic data object, and they are commonly seen in neuroscience, genetic studies, microbiome studies, and social cognitive studies. Such types of data bring statistical challenges that cannot be adequately addressed by existing tools. This project seeks to provide foundational perspectives on the emerging inferential and computational challenges in modeling a population and populations of networks. The theory and methods developed here will allow us to characterize the network connectivity at the population-level, and to monitor how the subject-level connectivity changes as a function of subject characteristics. Quantifying such subject-level differences has become central in studying the human brain, genetics, and medicine in general. Motivated by applications in neuroscience, this research will be beneficial for a variety of fields that study brain development, aging, and disease diagnosis, progression and treatment. Integration of research and education will be achieved through training undergraduate and graduate students, and developing special topics graduate courses.This project aims to develop a new network response model framework, in which the networks are treated as responses and the network-level covariates as predictors. The framework developed in this project, under appropriate structural constraints, will preserve the intrinsic characteristics of networks, ensure model identifiability, facilitate scalable computation, and allow valid statistical inference. A variety of fundamental and critical computational and inferential challenges will be addressed under this framework, including model identifiability, efficient computation, quantifying computational and statistical errors, and debiased inference. Additionally, the investigator will develop two novel goodness-of-fit tests for a broad class of network models, including those considered in this project. Further, the investigator will investigate modeling with heterogeneity by developing a network mixed-effect model, and a framework for model-based network clustering. Developments in both directions are formulated to take into account the rich information from subject covariates. The theory to be developed under asymptotic regimes allows the network size, the number of network samples, and the model complexity (e.g., rank, sparsity, number of clusters) to increase at reasonable rates.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.

在现代数据科学中，网络已成为非传统数据中最重要，无处不在的类型之一。最近，拥有大量独立网络价值样本的数据集已越来越多。在这样的数据集中，网络是基本数据对象，它们通常在神经科学，遗传研究，微生物组研究和社会认知研究中看到。这种类型的数据带来了现有工具无法充分解决的统计挑战。该项目旨在为建模网络人群和人群建模新兴的推论和计算挑战提供基本观点。这里开发的理论和方法将使我们能够表征人口级别的网络连接，并监视主题级别的连接如何随主体特征的函数而变化。量化这种主题级别的差异已成为研究人脑，遗传学和医学的核心。由神经科学中的应用激励，这项研究将对研究大脑发育，衰老和疾病诊断，进展和治疗的各种领域有益。研究和教育的融合将通过培训本科生和研究生，并开发特殊主题研究生课程来实现。该项目旨在开发一个新的网络响应模型框架，其中将网络视为响应，而网络级别的协变量作为预测因素。在适当的结构约束下，该项目中开发的框架将保留网络的内在特征，确保模型可识别性，促进可扩展计算并允许有效的统计推断。在此框架下将解决各种基本和关键的计算和推论挑战，包括模型可识别性，有效的计算，量化计算错误和统计错误以及依据推断。此外，研究人员将为广泛的网络模型开发两个新颖的合适性测试，包括该项目中考虑的网络模型。此外，研究者将通过开发网络混合效应模型以及基于模型网络聚类的框架来研究具有异质性的建模。这两个方向的发展都旨在考虑主题协变量的丰富信息。该理论在渐近方案下开发的理论允许网络规模，网络样本数量和模型复杂性（例如，等级，稀疏性，稀疏性，簇数）以合理的速度增加。该奖项反映了NSF的法定任务，并被认为是通过基金会的智力优点和广泛的影响来评估Criteria的智力优点和广泛影响。