Document Type

Dissertation - Open Access

Award Date

2021

Degree Name

Doctor of Philosophy (PhD)

Department

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 di 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.

Number of Pages

118

Publisher

South Dakota State University

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Rights Statement

In Copyright