权益分类	功能权益	普通用户	{{item.name}}会员
{{category.name}}	{{benefitItem.name}}

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，网络已成为最重要和最普遍的非传统数据类型之一。最近，具有大量独立网络值样本的数据集变得越来越可用。在此类数据集中，网络作为基本数据对象，常见于神经科学、遗传学研究、微生物组研究和社会认知研究中。此类数据带来了现有工具无法充分解决的统计挑战。该项目旨在为群体和网络群体建模中出现的推理和计算挑战提供基础视角。这里开发的理论和方法将使我们能够描述人口层面的网络连接性，并监测主体级连接性如何随着主体特征的变化而变化。量化这种学科层面的差异已成为研究人脑、遗传学和医学的核心。在神经科学应用的推动下，这项研究将有益于研究大脑发育、衰老以及疾病诊断、进展和治疗的各个领域。通过培养本科生和研究生，开发专题研究生课程，实现研究与教育的融合。该项目旨在开发一种新的网络响应模型框架，其中网络被视为响应，网络级协变量作为预测变量。该项目开发的框架在适当的结构约束下，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理挑战，包括模型可识别性、高效计算、量化计算和统计错误以及去偏推理。此外，研究人员将为广泛的网络模型（包括本项目中考虑的模型）开发两种新颖的拟合优度测试。此外，研究人员将通过开发网络混合效应模型和基于模型的网络聚类框架来研究异质性建模。两个方向的发展都是为了考虑到主题协变量的丰富信息而制定的。在渐进机制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、聚类数量）以合理的速度增加。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。