Open PRAIRIE: Open Public Research Access Institutional Repository and Information Exchange
Abstract
In spatial statistics, because quantities are correlated based on their relative positions in space, data is modeled as a single realization of a multivariate stochastic process. Spatial data can be high-dimensional either through a large number of observed variables per location, or through a large number of observed locations. The two are often handled differently, with the former addressed through dimension reduction and the latter addressed through appropriate modeling of the spatial correlation between locations. The main body of this dissertation is a three-part work. Parts 2 and 3 pertain to the many variables problem, proposing novel methods of dimension reduction for spatial data. Part 4 pertains to the many locations problem, using state-of-the-art techniques to analyze a massive satellite data set, improving on the current usage of the data
