Excellence in Research: Statistical Network Modeling and Inference for Complex Data

卓越的研究：复杂数据的统计网络建模和推理

• 批准号: 2100729
• 负责人: Seong-Tae Kim
• 金额: $ 78.34万
• 依托单位: North Carolina Agricultural & Technical State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-08-01 至 2024-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2100729&HistoricalAwards=false
• 关键词: Excellence; Research; Statistical; Network; Modeling

Estimation and inference of network structure have wide applications in many scientific fields such as genomics and finance. However, the abundance of complex data presents a great demand for new statistical learning methods in network analysis. A main goal of this project is to develop a set of novel methodological and theoretical tools to identify change points and infer structural changes for high-dimensional networks. Success of this project can have significant impacts on biomedical sciences and finance. Data applications to the Alzheimer's disease and portfolio risk monitoring will help to offer new insights. The team will develop computational packages to facilitate the application and dissemination of the proposed methods to academia and industry. Furthermore, the research will be closely integrated with education, through joint supervision of students and joint development of courses from two institutions. Underrepresented minority students will be recruited and involved in the project. The collaborative project will provide an opportunity for students and faculty in an HBCU institution to gain access to cutting-edge research and educational resources, and help increase the diversity of the next generation of data scientists.The research of this project has two main directions. The first one focuses on change point analysis for heterogenous data. To detect possible change points of a high-dimensional graph, a threshold variable and a threshold parameter are introduced while considering all nodes simultaneously to construct a highly effective algorithm. To simultaneously identify change points in a high-dimensional linear model, an innovative method to test homogeneity of the corresponding regression coefficients across different segments is considered. For the second direction, a nonparametric testing method is developed to compare correlation/covariance matrices. The team plans to investigate theoretical properties of the proposed methods and apply the methods to genomics and finance. This project can provide unique contributions to the statistical learning and big data literature. In addition, the knowledge gained from the proposed research can be valuable for handling other complex high dimensional problems in statistics and machine learning.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.

网络结构的估计和推断在基因组学、金融学等科学领域有着广泛的应用。然而，丰富的复杂数据提出了新的统计学习方法在网络分析中的巨大需求。该项目的主要目标是开发一套新颖的方法和理论工具，以识别高维网络的变点和推断结构变化。该项目的成功可能对生物医学科学和金融产生重大影响。阿尔茨海默病和投资组合风险监测的数据应用将有助于提供新的见解。该小组将开发计算软件包，以促进向学术界和工业界应用和传播拟议的方法。此外，研究将通过对学生的联合监督和两个机构的课程联合开发与教育紧密结合。代表性不足的少数民族学生将被招募并参与该项目。该合作项目将为HBCU机构的学生和教师提供一个获得前沿研究和教育资源的机会，并帮助增加下一代数据科学家的多样性。该项目的研究有两个主要方向。第一个是针对异质数据的变点分析。为了检测高维图的可能变化点，引入阈值变量和阈值参数，同时考虑所有节点，构造一个高效的算法。为了同时识别高维线性模型中的变点，考虑了一种新的方法来测试不同段之间相应回归系数的同质性。对于第二个方向，一个非参数检验方法来比较相关/协方差矩阵。该团队计划研究所提出的方法的理论特性，并将这些方法应用于基因组学和金融。该项目可以为统计学习和大数据文献提供独特的贡献。此外，从拟议的研究中获得的知识对于处理统计和机器学习中的其他复杂高维问题也很有价值。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

Feature Reduction Method Comparison Towards Explainability and Efficiency in Cybersecurity Intrusion Detection Systems

• DOI: 10.1109/icmla55696.2022.00211
• 发表时间: 2022-12
• 期刊: 2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
• 作者: Adam Lehavi;S. Kim

Learning Optimal Group-structured Individualized Treatment Rules with Many Treatments.

• 发表时间: 2023
• 期刊: Journal of machine learning research : JMLR
• 作者: Ma H;Zeng D;Liu Y
• 通讯作者: Liu Y

Simultaneous Change Point Inference and Structure Recovery for High Dimensional Gaussian Graphical Models

• 发表时间: 2021
• 作者: B. Liu

Multi-response Regression for Block-missing Multi-modal Data without Imputation

无插补的块缺失多模态数据的多响应回归

• DOI: 10.5705/ss.202021.0170
• 发表时间: 2024
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Wang, Haodong;Li, Quefeng;Liu, Yufeng
• 通讯作者: Liu, Yufeng

High Dimensional Change Point Inference: Recent Developments and Extensions

高维变点推断：最新发展和扩展

• DOI: 10.1016/j.jmva.2021.104833
• 发表时间: 2022
• 期刊: Journal of Multivariate Analysis
• 影响因子: 1.6
• 作者: Liu, B.