Space-time models, methods, and applications

自然界中的许多现象都是跨越空间和时间进行测量的。在存在不确定性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。没有机会研究这些在时间或空间上被边缘化的相互作用。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。研究人员研究的模型被定义为时空创新过程的空间相关过滤，即地质统计学过程的实现。创新不需要在时间上保持不变。这些模型包括通常类别的线性时间序列模型，以及标准的地质统计学模型，既有高斯模型，也有非高斯模型。这些过程不需要在时间或空间上是静止的，而是建立在关于非平稳模型的日益增长的文献之上。这项研究利用这些创新以及用于定义这些时空过程的过滤操作，将从空间统计和时间序列入手，开发时空背景下的推断和预测方法。冰芯古气候学涉及研究地球冰川和冰盖中保存的物理和化学性质。这些从冰芯中分析出来的特性被用作各种气候过程的替代物。研究人员与俄亥俄州立大学的其他研究人员合作，研究了从分布在格陵兰岛及其周围的冰芯的化学分析中提取的替代积累量。利用这项研究开发的模型，研究人员将检验这些累积记录与气候多变性和变化的已知驱动因素之间的关系。除了气候研究，这些成果将立即适用于其他科学领域，如言语和听力科学。调查结果将通过统计和专题领域的文章传达。在开展这项研究时，将对不同领域的学生(统计和非统计)进行空间和时间序列方法方面的教育。

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.