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.

在许多科学发现和当代应用中，尤其是在社交网络，营销，医学，遗传学和癌症研究等领域，了解变量如何相互相互作用至关重要。确定重要的互动还可以帮助改善模型的解释性和预测。然而，与高维数据的相互作用检测构成了巨大的挑战，因为成对相互作用的数量随着协变量的数量和高阶相互作用的数量而增加，而高阶相互作用的增长速度更快。尽管关于互动检测的文献越来越多，但在错误率控制和推理方面的工作数量有限。建立互动检测和非参数推断的强大统计基础，并提供可再现和可扩展的算法来选择重要的互动，可以极大地促进这些急需的工具在实际应用中的使用。整个项目的基础主题是开发有关高维相互作用检测的统计方法和理论，与统计保证的非参数推断以及改善的可重复性和可解释性。该项目具有三个相互关联的有关及时的理论和方法学研究的目的检测和非参数推断。第一个目的建立了超高维回归模型中相互作用检测的预测和错误标志率控制的理论基础。第二个目标是建立在Model-X仿冒品的最新发展基础上，并提出了通过错误的发现率控制和吸引力的高维相互作用检测的新方法。第三个目标进一步研究了一对高维随机矢量之间的非线性相互作用，并通过距离相关镜头开发了一种新的测试程序，用于高维非参数推断。上面三个目标中开发的系统研究计划将有助于建立严格的统计基础理论和方法论，用于高维数据分析，可以指导从业者和研究人员。调查人员还计划系统地开发可易和高效的计算算法，以通过R和Python（例如R和Python）实施所提出的方法，然后使其易于使用，并在所有相关领域中宣传它们。此奖项反映了NSF的法规任务，并已一直是认为值得通过基金会的智力优点和更广泛影响的评论标准来评估值得支持。

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