Collaborative Research: Halfspace Depth for Object and Functional Data

协作研究：对象和功能数据的半空间深度

• 批准号: 2113696
• 负责人: Sara Lopez-Pintado
• 金额: $ 17.5万
• 依托单位: Northeastern 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: 2021-07-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2113696&HistoricalAwards=false
• 关键词: Collaborative; Research; Halfspace; Depth; Object

Complex data objects are increasingly being generated across science and engineering. Non-Euclidean data such as wind directions, neural connectivity networks, and phylogenetic trees draw practical interest, but are challenging to analyze due to their intrinsic constraints. Functional data such as trajectories and images also provide examples of another type of data of high complexity, which are observed on a continuous domain in time or space. In general, practitioners are interested in first exploring the data distributions before any modeling analysis. For instance, given a sample of growth trajectories of children, a first step is to identify typical versus extreme growth patterns, where the latter can be non-trivial to uncover. Also, when analyzing brain connectivity matrices, it is important to find unusual brain networks and differences between healthy and diseased populations. Data-driven methods robust to anomalies are essential in these settings since little is known about the data generating process, and outliers can affect the analysis. Due to the lack of a natural ordering in data objects, exploratory tools such as boxplot and quantile are unavailable for these types of data. The project will address the lack of techniques for exploring non-Euclidean and functional data. Principled statistics and visualization methods will be developed based on a novel way of ranking the observations. The project will also provide training for graduate and undergraduate students. The central research theme is to develop exploratory data analysis tools for non-Euclidean and functional data objects. To overcome the absence of a canonical ordering for object data, the PIs will develop suitable data depth notions to quantify the centrality of data points with respect to the distribution. This will provide a center-outward ranking of the data that will be used as a building block for outlier detection methods, rank tests, and robust classifiers. Analogous to Tukey's halfspace depth for the multivariate Euclidean case, the new depth notions for object data are expected to be intuitive and robust, and have desirable properties well-grounded in theory. Specifically, the research project will investigate a depth notion for non-Euclidean objects; a data visualization and an outlier detection procedure for non-Euclidean data; halfspace depth notions for functional data, one based on theory and another one from an algorithmic perspective; and a depth notion for sparsely observed longitudinal data. Key challenges that will be addressed include a lack of vector space structure when dealing with non-Euclidean objects; the infinite dimensionality and degeneracy when defining depth notions for functional data; detecting outlying trajectories and images in shape and not just at any time point; and the sparsity and irregularity of observations in longitudinal data. Method and theory development will draw from metric geometry, functional data analysis, empirical process, and M-estimation. Software implementing a suite of depth-based methods will be made available to the public as an outcome of the project.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.

复杂的数据对象越来越多地在科学和工程中产生。非欧几里德数据，如风向，神经连接网络和系统发育树引起了实际的兴趣，但由于其内在的限制，分析起来很有挑战性。诸如轨迹和图像之类的功能数据也提供了另一种类型的高复杂性数据的示例，其在时间或空间的连续域上被观察到。一般来说，从业者感兴趣的是在任何建模分析之前首先探索数据分布。例如，给定一个儿童成长轨迹的样本，第一步是识别典型的与极端的成长模式，其中后者可能是不可忽视的。此外，在分析大脑连接矩阵时，重要的是要找到不寻常的大脑网络以及健康和患病人群之间的差异。在这些环境中，对异常具有鲁棒性的数据驱动方法是必不可少的，因为对数据生成过程知之甚少，并且离群值可能会影响分析。由于数据对象缺乏自然的排序，因此箱线图和分位数等探索性工具无法用于这些类型的数据。该项目将解决缺乏探索非欧几里德和函数数据的技术问题。原则性的统计和可视化方法将根据一种新的方式排名的意见。该项目还将为研究生和本科生提供培训。中心研究主题是为非欧几里得和函数数据对象开发探索性数据分析工具。为了克服对象数据缺乏规范排序的问题，PI将开发适当的数据深度概念，以量化数据点相对于分布的中心性。这将提供一个中心向外的数据排名，将被用作离群值检测方法，秩检验和鲁棒分类器的构建块。类似于Tukey的半空间深度的多变量欧几里德的情况下，对象数据的新的深度概念预计是直观的和强大的，并具有理想的属性良好的理论基础。具体来说，该研究项目将研究非欧几里德对象的深度概念;非欧几里德数据的数据可视化和离群值检测程序;函数数据的半空间深度概念，一个基于理论，另一个从算法的角度;以及稀疏观测纵向数据的深度概念。将解决的关键挑战包括缺乏向量空间结构时，处理非欧几里德对象;无限的维数和退化定义功能数据的深度概念时;检测形状，而不仅仅是在任何时间点的外围轨迹和图像;和稀疏性和不规则性的纵向数据的观察。方法和理论的发展将借鉴度量几何，功能数据分析，经验过程，和M-估计。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

Tukey’s Depth for Object Data

• DOI: 10.1080/01621459.2021.2011298
• 发表时间: 2021-09
• 期刊: Journal of the American Statistical Association
• 影响因子: 3.7
• 作者: Xiongtao Dai;S. López-Pintado