Space-time models, methods, and applications

时空模型、方法和应用

• 批准号: 0604963
• 负责人: Peter Craigmile
• 金额: $ 9.53万
• 依托单位: Ohio State University Research Foundation -DO NOT USE
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-07-01 至 2010-09-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0604963&HistoricalAwards=false
• 关键词: Space; time; models; methods; applications

Many phenomena in nature are measured across space and through time. Investigating possible space-time interactions, in the presence of uncertainty, is key to understanding the science. There is no opportunity to study these interactions when marginalized over time or space. Space-time statistical methodologies should be faithful to methodology in time series analysis and spatial statistics. The models the investigator studies are defined as spatially-dependent filterings of space-time innovation processes that are realizations of geostatistical processes. The innovations do not need to be invariant with respect to time. These models include the usual class of linear time series models, as well as standard geostatistical models, both Gaussian and non-Gaussian. The processes do not need to be stationary in time or in space, and build on the growing literature concerning nonstationary models. Leveraging both the innovations, as well as the filtering operations used to define these space-time processes, this research will springboard from spatial statistics and time series to develop methods for inference and prediction in a space-time context.Ice core paleoclimatology involves the study of the physical and chemical properties preserved in the Earth's glaciers and ice sheets. These properties, analyzed from ice cores, are used as proxies for various climatological processes. In collaboration with other researchers at The Ohio State University, the investigator studies proxy measures of accumulation, extracted from chemical analyses of ice cores distributed over and around Greenland. Using the models developed under this research the investigator will examine relationships between these accumulation records and known drivers of climate variability and change. In addition to climate research, the results will be immediately applicable to other scientific areas, such as speech and hearing sciences. Findings will be communicated via articles in statistical and subject-matter areas. Education of a diverse cross-section of students (statistical and non-statistical), in spatial and time series methodologies, will be achieved in carrying out this research.

自然界的许多现象都是通过空间和时间来测量的。在不确定性存在的情况下，研究可能的时空相互作用是理解这门科学的关键。当这些相互作用在时间或空间上被边缘化时，就没有机会研究它们。时空统计方法应忠实于时间序列分析和空间统计的方法论。研究者研究的模型被定义为时空创新过程的空间依赖滤波，时空创新过程是地质统计过程的实现。创新并不需要在时间上保持不变。这些模型包括通常的线性时间序列模型，以及标准的高斯和非高斯地质统计模型。这些过程不需要在时间或空间上是平稳的，并且建立在关于非平稳模型的日益增长的文献基础上。利用这两种创新，以及用于定义这些时空过程的过滤操作，本研究将从空间统计和时间序列出发，开发时空背景下的推理和预测方法。冰芯古气候学涉及对地球冰川和冰原中保存的物理和化学性质的研究。从冰芯中分析的这些特性被用作各种气候过程的代用物。在与俄亥俄州立大学的其他研究人员的合作下，这位研究人员从格陵兰岛及其周围分布的冰芯的化学分析中提取了积累的替代测量方法。利用本研究开发的模式，研究者将检验这些积累记录与气候变率和变化的已知驱动因素之间的关系。除气候研究外，研究结果将立即应用于其他科学领域，如语言和听力科学。调查结果将通过统计和主题领域的文章进行交流。在进行这项研究时，将以空间和时间序列方法对不同截面的学生（统计和非统计）进行教育。