Structural Learning and Statistical Inference for Large-Scale Data

大规模数据的结构学习和统计推断

• 批准号: 2013486
• 负责人: Chunming Zhang
• 金额: $ 12万
• 依托单位: University of Wisconsin-Madison
• 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=2013486&HistoricalAwards=false
• 关键词: Structural; Learning; Statistical; Inference; Large

This project aims at developing new structure learning and statistical inference procedures for capturing some essential structures of large-scale data, emerging from scientific studies in genetics, biology, neuroscience, finance, and meteorology, among others. New tools for stochastic modeling, computational algorithms, and statistical inference applied to multi-channel brain EEG recordings, multi-subject fMRI and multiple neuron spike trains in neuroscience research, and identifying structural changes in climate data and copy number variation in genetics, will be developed. Outcomes of this study will help scientists to efficiently analyze large-scale imaging, temporal and spatial data, and thus will have broader impacts on our society through their direct impacts on these applications to science, public health, and information technology. Dissemination of these developments will enhance new knowledge discoveries, and strengthen interdisciplinary collaborations. The research will also be integrated with educational practice through designing either regular, seminar or short courses on new statistical approaches for analyzing complex data as well as benefitting the training and learning of undergraduate, graduate students and underrepresented minorities.This research work focuses on statistical learning of fundamentally distinct types of structures, with the ultimate goal of better understanding of complex systems. Motivated from inferring neural connectivity from the ensemble neural spike train data, Project 1 will learn the directed acyclic graph structure in a large Poisson network, underlying a wide array of multivariate point process data. The related probabilistic mechanism will provide new insights into understanding statistical properties of the estimators for graph parameters relevant to mining the causal relation among neurons. Inspired by feature extraction and source separation from multi-channel brain EEG recordings and non-linear temporal signal processing, Project 2 will develop a class of non-linear non-smooth combinations of structured component analysis (SCA) to extract hidden component signals from observed mixed signals. The SCA developed will be more broadly applicable in scientific studies. Motivated from identifying and understanding structural changes in climate trends, and structural variation in gene copy numbers associated with genetic diseases, Project 3 will develop a novel two-step adaptive procedure of jump detection, for simultaneously selecting the unknown number of jump points and detecting their locations in the flexible non-parametric regression model.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.

该项目旨在开发新的结构学习和统计推断程序，用于捕获大规模数据的一些基本结构，这些数据来自遗传学，生物学，神经科学，金融和气象学等科学研究。将开发用于随机建模、计算算法和统计推断的新工具，这些工具适用于神经科学研究中的多通道脑EEG记录、多学科功能磁共振成像和多神经元尖峰序列，以及识别气候数据中的结构变化和遗传学中的拷贝数变异。这项研究的结果将帮助科学家有效地分析大规模成像，时间和空间数据，从而通过对这些应用的直接影响对我们的社会产生更广泛的影响科学，公共卫生和信息技术。这些发展的传播将促进新的知识发现，并加强跨学科合作。该研究还将通过设计用于分析复杂数据的新统计方法的定期，研讨会或短期课程与教育实践相结合，并使本科生，研究生和代表性不足的少数民族的培训和学习受益。这项研究工作侧重于基本不同类型结构的统计学习，最终目标是更好地理解复杂系统。从集成神经尖峰序列数据推断神经连接性的动机，项目1将学习大型泊松网络中的有向非循环图结构，该网络是各种多变量点过程数据的基础。相关的概率机制将提供新的见解，了解统计特性的估计相关的挖掘神经元之间的因果关系的图参数。受多通道脑电记录和非线性时间信号处理的特征提取和源分离的启发，项目2将开发一类非线性非平滑结构化成分分析（SCA）组合，从观察到的混合信号中提取隐藏成分信号。所开发的SCA将更广泛地适用于科学研究。从识别和理解气候趋势的结构变化，以及与遗传疾病相关的基因拷贝数的结构变化出发，项目3将开发一种新的两步自适应跳跃检测程序，用于同时选择未知数目的跳跃点并检测它们在柔性非接触区域中的位置，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

On simultaneous calibration of two-sample t-tests for high-dimension low-sample-size data

高维低样本数据双样本t检验的同时校准

• DOI: 10.5705/ss.202018.0467
• 发表时间: 2021
• 期刊: Statistica Sinica
• 影响因子: 1.4
• 作者: Zhang, Chunming;Jia, Shengji;Wu, Yongfeng
• 通讯作者: Wu, Yongfeng

Robust estimation in regression and classification methods for large dimensional data

• DOI: 10.1007/s10994-023-06349-2
• 发表时间: 2023-07
• 期刊: Machine Learning
• 影响因子: 7.5
• 作者: Chunming Zhang;Lixing Zhu;Yanbo Shen

Empirical likelihood inference in autoregressive models with time-varying variances

• DOI: 10.1080/24754269.2021.1913977
• 发表时间: 2021-04
• 期刊: Statistical Theory and Related Fields
• 影响因子: 0.5
• 作者: Yu Han;Chunming Zhang

Assessment of Projection Pursuit Index for Classifying High Dimension Low Sample Size Data in R

R 中高维低样本量数据分类的投影追踪指数评估

• DOI: 10.6339/23-jds1096
• 发表时间: 2023
• 期刊: Journal of Data Science
• 作者: Wu, Zhaoxing;Zhang, Chunming
• 通讯作者: Zhang, Chunming

Further Examples Related to Correlations Between Variables and Ranks

与变量和排名之间的相关性相关的更多示例

• DOI: 10.1080/00031305.2020.1831956
• 发表时间: 2021
• 期刊: The American Statistician
• 作者: Zhang, Chunming