Document Type
Dissertation - Open Access
Award Date
2021
Degree Name
Doctor of Philosophy (PhD)
Department / School
Mathematics and Statistics
First Advisor
Hossein Moradi Rekabdarkolaee
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.
Library of Congress Subject Headings
Spatial analysis (Statistics)
Geospatial data.
Number of Pages
118
Publisher
South Dakota State University
Recommended Citation
May, Paul, "Methods for High-Dimensional Spatial Data: Dimension Reduction and Covariance Approximation" (2021). Electronic Theses and Dissertations. 223.
https://openprairie.sdstate.edu/etd2/223
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