Estimation and Inference for Massive Multivariate Spatial Data

海量多元空间数据的估计和推理

• 批准号: 1844420
• 负责人: Joseph Guinness
• 金额: $ 10.27万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-05-15 至 2020-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1844420&HistoricalAwards=false
• 关键词: Estimation; Inference; Massive; Multivariate; Spatial

Satellite observations of the Earth's atmosphere and oceans have the potential to improve forecasting of hurricanes and other extreme weather events. Massive efforts to sample the chemical constituents present in well water can reduce uncertainty in mapping of hazardous materials in groundwater. Observations of chemical reactions at the sub-micron scale may lead to new insights about the behavior of toxic trace elements in soils. However, the value of these expensive efforts to collect massive amounts of data will not be fully realized if the statistical techniques for analyzing them do not keep pace. The current techniques available are inadequate to flexibly model and extract information from massive datasets consisting of many variables collected across a region. This research project aims to develop computationally efficient methods for addressing the central challenges for analyzing massive multivariate spatial data: (1) drawing justifiable conclusions about the relationships among the multiple variables, and (2) making full and appropriate use of all variables when mapping the data. Addressing the first challenge is essential to translating observational and experimental data into scientific knowledge. Addressing the second is crucially important for providing predictions of potentially harmful outcomes, and the key to solving both challenges is integrating the multivariate and spatial data analysis into a unified framework.The inherent correlation in time series and spatial data is the feature that makes interpolation and forecasting possible, but it also complicates estimation of multivariate relationships. As a result, analyses of time series data often start with a transformation of the data into the spectral domain, in which the transformed data are approximately uncorrelated. Although the spectral domain has played a central role in developing theory for models for spatial data, several issues have hindered the implementation of practical spectral domain methods for spatial data. This project aims to develop methodological innovations to overcome those barriers and provide practitioners with a flexible set of tools to extract information from dozens of spatial variables simultaneously, and predict variables at unsampled locations using all of the available data. The methods employ computationally efficient periodic data augmentations to simplify analyses, dramatically improve the ability to characterize uncertainty, and are supported by novel theoretical results.

对地球大气层和海洋的卫星观测有可能改进对飓风和其他极端天气事件的预报。大量努力对井水中的化学成分进行取样，可以减少地下水中有害物质绘图的不确定性。在亚微米尺度上观察化学反应可能会导致对土壤中有毒微量元素行为的新见解。然而，如果用于分析数据的统计技术跟不上步伐，这些收集大量数据的昂贵努力的价值将无法充分实现。现有的技术不足以灵活地建模和提取信息，从大量的数据集，包括许多变量收集在一个区域。该研究项目旨在开发计算效率高的方法，以解决分析大量多元空间数据的核心挑战：（1）对多个变量之间的关系得出合理的结论，以及（2）在映射数据时充分和适当地使用所有变量。应对第一个挑战对于将观测和实验数据转化为科学知识至关重要。解决第二个问题对于提供潜在有害结果的预测至关重要，解决这两个挑战的关键是将多元和空间数据分析集成到一个统一的框架中。时间序列和空间数据的内在相关性是插值和预测成为可能的特征，但它也使多元关系的估计变得复杂。因此，时间序列数据的分析通常从数据到谱域的变换开始，其中变换后的数据近似不相关。虽然谱域在空间数据模型理论的发展中发挥了核心作用，但一些问题阻碍了空间数据实用谱域方法的实施。该项目旨在开发方法创新，以克服这些障碍，并为从业人员提供一套灵活的工具，以同时从数十个空间变量中提取信息，并利用所有现有数据预测未抽样地点的变量。该方法采用计算效率高的周期性数据扩充，以简化分析，显着提高表征不确定性的能力，并支持新的理论结果。