Statistical Models and Methods for Complex Data in Metric Spaces

度量空间中复杂数据的统计模型和方法

• 批准号: 2310450
• 负责人: Hans-Georg Mueller
• 金额: $ 33.58万
• 依托单位: University of California-Davis
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-07-01 至 2026-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2310450&HistoricalAwards=false
• 关键词: Statistical; Models; Methods; Complex; Data

Big data in the form of random objects are increasingly encountered across sciences and society. Adequate and principled approaches for their analysis are important to extract relevant information, to make predictions, and to assess whether there are differences between samples. However, the complexity of such data poses major challenges for statistical analysis and traditional methods cannot be applied. In this project, these challenges will be addressed and overcome through the development of advanced nonparametric statistical methodology that can handle the complexity of object data. Specific objects that will be studied include networks and distributions, with applications for brain imaging and genomics data, climate change data and data from other areas of current interest. For situations where random objects are repeatedly observed over time, such as repeatedly observed MRI brain scans for the same subject, quantifications of the underlying time dynamics will also be developed. The research will include methods for testing and estimation, theory, efficient computational implementation, and data applications, which are expected to lead to substantial new insights. The project will provide training for the next generation of data analysts and research statisticians and user-friendly code implementing the methods will be made available. The project will contribute to the foundations of the emerging field of metric statistics as a comprehensive framework for statistical methodology and theory for samples of random objects, which are random variables/data that take values in a metric space. Random objects encompass data in the form of distributions, networks, trees, covariance matrices and surfaces, and data on Riemannian manifolds such as spheres. The statistical analysis of such data is challenging as one cannot apply traditional statistical methods due to the absence of a vector space structure. Specifically, for random objects that are situated in a geodesic space, a general class of regression models for transports from the barycenter to specific objects will be developed. In these models both predictors and responses are transports and specific examples are regression models for spherical data and distributions. The project will also include the study of regression models for multivariate distributions as responses, paired with Euclidean predictors, using slice-optimization in Wasserstein space. This approach will be illustrated with applications in climatology and life sciences data. Another goal is the development of transport processes in a geodesic space, constituting a new type of stochastic process. For such processes, anchor point representations for general types of random objects and latent Gaussian process representations for distributional objects in Wasserstein space will be obtained. Additionally, a random effects model for Frechet regression will be developed, providing the first such model for longitudinal object data, with applications in brain imaging and distributional data analysis. Throughout, the challenges resulting from the absence of Euclidean and algebraic structures will be addressed with empirical process theory and other tools.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.

随机对象形式的大数据越来越多地出现在科学和社会中。适当和有原则的分析方法对于提取相关信息、进行预测和评估样本之间是否存在差异非常重要。然而，这些数据的复杂性给统计分析带来了重大挑战，传统方法无法应用。 在这个项目中，这些挑战将通过开发先进的非参数统计方法来解决和克服，这些方法可以处理对象数据的复杂性。 将研究的具体对象包括网络和分布，以及脑成像和基因组学数据、气候变化数据和当前感兴趣的其他领域的数据的应用。对于随着时间的推移重复观察随机对象的情况，例如重复观察同一受试者的MRI脑部扫描，还将开发潜在时间动态的量化。该研究将包括测试和估计方法，理论，有效的计算实现和数据应用，预计将导致大量的新见解。该项目将为下一代数据分析员和研究统计员提供培训，并将提供便于用户使用的代码来执行这些方法。 该项目将为新兴的度量统计领域奠定基础，作为随机对象样本的统计方法和理论的综合框架，随机对象是在度量空间中取值的随机变量/数据。随机对象包括分布、网络、树、协方差矩阵和曲面形式的数据，以及黎曼流形（如球面）上的数据。对这些数据的统计分析是具有挑战性的，因为由于缺乏向量空间结构，人们无法应用传统的统计方法。 具体而言，对于位于测地线空间中的随机对象，将开发从重心到特定对象的一般类回归模型。在这些模型中，预测因子和响应都是传输，具体示例是球形数据和分布的回归模型。该项目还将包括研究多元分布的回归模型作为响应，与欧几里德预测因子配对，使用Wasserstein空间中的切片优化。这一方法将在气候学和生命科学数据中得到应用。另一个目标是在测地空间中发展运输过程，构成一种新型的随机过程。对于这样的过程，一般类型的随机对象的锚点表示和Wasserstein空间中的分布对象的潜高斯过程表示将被获得。 此外，还将开发一个用于Frechet回归的随机效应模型，为纵向对象数据提供第一个此类模型，并应用于脑成像和分布数据分析。 在整个过程中，由于缺乏欧几里德和代数结构而产生的挑战将通过经验过程理论和其他工具来解决。该奖项反映了NSF的法定使命，并被认为值得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来支持。