Introduction to the statistical modeling of spatial and time series data
Introduction to modeling of data with spatial and/or time dependence. This course introduces methods and tools for manipulating, exploring, and modeling spatial (point referenced and areal) and time series data (discrete and continuous). Some of the key modeling techniques covered include: Gaussian processes and their generalizations; CAR, SAR, IAR, and kriging models; ARM, ARMA, and dynamic linear models. The course will cover the underlying statistical theory behind these models as well as the use of computational tools for the application of these models to real data. Prerequisites: STA 210 and one of (STA 230, 231 or 240).