High-Dimensional Interaction Detection and Nonparametric Inference

高维交互检测和非参数推理

• 批准号: 1953356
• 负责人: Jinchi Lv
• 金额: $ 10万
• 依托单位: University of Southern California
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-15 至 2023-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1953356&HistoricalAwards=false
• 关键词: Dimensional; Interaction; Detection; Nonparametric; Inference

Understanding how variables interact with each other is fundamentally important in many scientific discoveries and contemporary applications, especially in areas such as social networks, marketing, medicine, genetics, and cancer studies. Identifying important interactions can also help improve model interpretability and prediction. Yet interaction detection with high-dimensional data poses great challenges since the number of pairwise interactions increases quadratically with the number of covariates and that of higher-order interactions grows even faster. Although there is a growing literature on interaction detection, there is a limited amount of work on the error rate control and inference aspects. Building robust statistical foundations of interaction detection and nonparametric inference, and offering reproducible and scalable algorithms for selecting important interactions can greatly facilitate the use of these much-needed tools in real applications. The common theme underlying this entire project is that of developing statistical methodologies and theories on high-dimensional interaction detection and nonparametric inference with statistical guarantees and improved reproducibility and interpretability.This project has three interrelated aims of timely theoretical and methodological studies on high-dimensional interaction detection and nonparametric inference. The first aim establishes the theoretical foundation of prediction and false sign rate control for interaction detection in ultra-high dimensional regression models. The second aim builds on the recent development of model-X knockoffs and proposes new methods for high-dimensional interaction detection with false discovery rate control and appealing power. The third aim further investigates the nonlinear interactions between a pair of high-dimensional random vectors and develops a new testing procedure for high-dimensional nonparametric inference through the lens of distance correlation. The systematic research program developed in three aims above will help build rigorous statistical foundations of theory and methodologies for high-dimensional data analysis that can guide practitioners and researchers. The investigators also plan to systematically develop tractable and efficient computation algorithms to implement the proposed methods through free software packages, like R and Python, and then make them readily available and publicize them in all relevant fields.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.

了解变量如何相互作用对于许多科学发现和当代应用至关重要，特别是在社交网络、营销、医学、遗传学和癌症研究等领域。识别重要的交互也有助于提高模型的可解释性和预测。然而，高维数据的交互检测提出了巨大的挑战，因为成对交互的数量随着协变量的数量呈二次方增加，而高阶交互的增长甚至更快。尽管关于交互检测的文献越来越多，但在错误率控制和推理方面的工作量有限。建立交互检测和非参数推理的强大统计基础，并提供可重复和可扩展的算法来选择重要的交互，可以极大地促进这些急需的工具在实际应用中的使用。整个项目的共同主题是开发高维交互检测和非参数推理的统计方法和理论，并提供统计保证并提高可重复性和可解释性。该项目具有三个相互关联的目标，即及时对高维交互检测和非参数推理进行理论和方法学研究。第一个目标为超高维回归模型中交互检测的预测和错误符号率控制建立理论基础。第二个目标以 Model-X 仿制品的最新发展为基础，提出了具有错误发现率控制和吸引力的高维交互检测新方法。第三个目标进一步研究一对高维随机向量之间的非线性相互作用，并通过距离相关性的透镜开发一种用于高维非参数推理的新测试程序。针对上述三个目标而开发的系统研究计划将有助于为高维数据分析建立严格的理论和方法统计基础，以指导从业者和研究人员。研究人员还计划系统地开发易于处理且高效的计算算法，通过 R 和 Python 等免费软件包来实施所提出的方法，然后将其公开并在所有相关领域公开。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。

项目成果

Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request

• DOI: 10.1287/isre.2021.0999
• 发表时间: 2021-05
• 期刊: Inf. Syst. Res.
• 作者: Ni Huang;Probal Mojumder;Tianshu Sun;Jinchi Lv;Joseph M. Golden

DeepLINK: Deep learning inference using knockoffs with applications to genomics

• DOI: 10.1073/pnas.2104683118
• 发表时间: 2021-09
• 期刊: Proceedings of the National Academy of Sciences
• 作者: Zifan Zhu;Yingying Fan;Yinfei Kong;Jinchi Lv;Fengzhu Sun

Large-scale model selection in misspecified generalized linear models

• DOI: 10.1093/biomet/asab005
• 发表时间: 2022-02-01
• 期刊: BIOMETRIKA
• 影响因子: 2.7
• 作者: Demirkaya, Emre;Feng, Yang;Lv, Jinchi
• 通讯作者: Lv, Jinchi

SIMPLE: Statistical inference on membership profiles in large networks

简单：对大型网络中的成员资料进行统计推断

• DOI: 10.1111/rssb.12505
• 发表时间: 2022
• 期刊: Journal of the Royal Statistical Society: Series B (Statistical Methodology
• 作者: Fan, Jianqing;Fan, Yingying;Han, Xiao;Lv, Jinchi
• 通讯作者: Lv, Jinchi