讲座题目：Statistical Models for Large Spatial and Spatio-Temporal Datasets
主讲人：康蕾（Emily Lei Kang）教授
主持人：余柏蒗 教授
时间：2016年6月2日（周四）14:00
讲座地址：闵行校区资环楼354报告厅
主办单位：地理科学学院，地理信息科学教育部重点实验室
报告人简介：
Dr. Emily L. Kang is Assistant Professor in Statistics it the Department of Mathematical Sciences at the University of Cincinnati. She received Ph.D. in Statistics in 2009 from The Ohio State University under the guidance of Dr. Noel Cressie. Her research interests include spatial and spatio-temporal statistics, hierarchical modeling, uncertainty quantification and their applications in environmental and engineering sciences.
报告简介：
With the development of modern technologies such as Geographical Information Systems (GIS) and Global Positioning Systems (GPS) routinely identifying geographical coordinates, scientists and researchers in a variety of disciplines today have access to geocoded data as never before, and such data become increasingly high-dimensional in terms of the number of observed locations (and over time). Spatial statistics for very large and massive datasets is challenging, since the size of the dataset causes problems in computing optimal spatial predictors, such as kriging. In addition, when a dataset is collected on a large spatial domain, the associated spatial process of interest typically exhibits nonstationary behavior over that domain, and a flexible family of nonstationary spatial dependence structure is preferred in statistical models. I will first introduce the statistical challenges and their developments in analyzing large or massive spatial and spatio-temporal data, then talk about some recent work I have done in this field. Specifically, I will discuss (1) statistical methods for prediction and downscaling; (2) statistical methods for data fusion. Applications of these methods will also be discussed.