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

• 批准号: 2015190
• 负责人: Emma Jingfei Zhang
• 金额: $ 19.25万
• 依托单位: University of Miami
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-07-01 至 2023-05-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2015190&HistoricalAwards=false
• 关键词: Statistical; Modeling; Inference; Network; Data

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，网络已经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。在这样的数据集中，网络作为基本的数据对象，它们通常出现在神经科学，遗传学研究，微生物组研究和社会认知研究中。这类数据带来了现有工具无法充分应对的统计挑战。该项目旨在为人口和网络人口建模中出现的推理和计算挑战提供基础观点。这里开发的理论和方法将使我们能够在人口水平上的网络连接的特点，并监测学科水平的连接性如何变化的主题特征的函数。量化这种学科层面的差异已经成为研究人类大脑、遗传学和医学的核心。受神经科学应用的启发，这项研究将有益于研究大脑发育，衰老和疾病诊断，进展和治疗的各种领域。本研究将通过培养本科生和研究生，以及开发专题研究生课程来实现研究与教育的一体化。本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，网络水平的协变量作为预测变量。在适当的结构约束下，该项目开发的框架将保留网络的内在特征，确保模型的可识别性，促进可扩展计算，并允许有效的统计推断。各种基本的和关键的计算和推理的挑战将在这个框架下，包括模型的可识别性，有效的计算，量化计算和统计错误，和去偏推理。此外，研究人员将为广泛的网络模型开发两种新的拟合优度测试，包括本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。两个方向的发展制定考虑到丰富的信息，从主题协变量。在渐近状态下开发的理论允许网络大小、网络样本的数量和模型复杂性（例如，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

Nonparametric estimation of the pair correlation function of replicated inhomogeneous point processes

• DOI: 10.1214/20-ejs1755
• 发表时间: 2020
• 期刊: Electronic Journal of Statistics
• 影响因子: 1.1
• 作者: Ganggang Xu;Chong Zhao;A. Jalilian;R. Waagepetersen;Jingfei Zhang;Yongtao Guan

Sparse Tensor Additive Regression

• 发表时间: 2019-03
• 期刊: J. Mach. Learn. Res.
• 作者: Botao Hao;Boxiang Wang;Pengyuan Wang;Jingfei Zhang;Jian Yang;W. Sun

Semi-parametric Learning of Structured Temporal Point Processes

结构化时间点过程的半参数学习

• 发表时间: 2020-09
• 期刊: Journal of Machine Learning Research
• 影响因子: 6
• 作者: Xu Ganggang;Wang Ming;Bian Jiangze;Huang Hui;Burch Timothy R.;Andrade S;ro C.;Zhang Jingfei;Guan Yongtao
• 通讯作者: Guan Yongtao