ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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grass) offers unique opportunities to mitigate climate change through avoided fossil fuel use and associated greenhouse gas reduction. Although conversion of existing agriculturally intensive lands (e.g., maize and soy) to perennial bioenergy cropping systems has been shown to reduce near-surface temperatures, unintended consequences on natural water resources via depletion of soil moisture may offset these benefits. In the effort of the cross-fertilization across the disciplines of physics-based modeling and spatio-temporal statistics, three topics are investigated in this dissertation aiming to provide a novel quantification and robust justifications of the hydroclimate impacts associated with bioenergy crop expansion. Topic 1 quantifies the hydroclimatic impacts associated with perennial bioenergy crop expansion over the contiguous United States using the Weather Research and Forecasting Model (WRF) dynamically coupled to a land surface model (LSM). A suite of continuous (2000–09) medium-range resolution (20-km grid spacing) ensemble-based simulations is conducted. Hovmöller and Taylor diagrams are utilized to evaluate simulated temperature and precipitation. In addition, Mann-Kendall modified trend tests and Sieve-bootstrap trend tests are performed to evaluate the statistical significance of trends in soil moisture differences. Finally, this research reveals potential hot spots of suitable deployment and regions to avoid. Topic 2 presents spatio-temporal Bayesian models which quantify the robustness of control simulation bias, as well as biofuel impacts, using three spatio-temporal correlation structures. A hierarchical model with spatially varying intercepts and slopes display satisfactory performance in capturing spatio-temporal associations. Simulated temperature impacts due to perennial bioenergy crop expansion are robust to physics parameterization schemes. Topic 3 further focuses on the accuracy and efficiency of spatial-temporal statistical modeling for large datasets. An ensemble of spatio-temporal eigenvector filtering algorithms (hereafter: STEF) is proposed to account for the spatio-temporal autocorrelation structure of the data while taking into account spatial confounding. Monte Carlo experiments are conducted. This method is then used to quantify the robustness of simulated hydroclimatic impacts associated with bioenergy crops to alternative physics parameterizations. Results are evaluated against those obtained from three alternative Bayesian spatio-temporal specifications.
munity. With life insurance policies and annuity products as dominant financial
instruments which depend on future mortality rates, there is a risk that observed
human mortality experiences will differ from projected when they are sold. From an
insurer’s portfolio perspective, to curb this risk, it is imperative that models of hu
man survivorship are constantly being updated and equipped to accurately gauge and
forecast mortality rates. At present, the majority of actuarial research in mortality
modeling involves factor-based approaches which operate at a global scale, placing
little attention on the determinants and interpretable risk factors of mortality, specif
ically from a spatial perspective. With an abundance of research being performed
in the field of spatial statistics and greater accessibility to localized mortality data,
there is a clear opportunity to extend the existing body of mortality literature to
wards the spatial domain. It is the objective of this dissertation to introduce these
new statistical approaches to equip the field of actuarial science to include geographic
space into the mortality modeling context.
First, this dissertation evaluates the underlying spatial patterns of mortality across
the United States, and introduces a spatial filtering methodology to generate latent
spatial patterns which capture the essence of these mortality rates in space. Second,
local modeling techniques are illustrated, and a multiscale geographically weighted
regression (MGWR) model is generated to describe the variation of mortality rates
across space in an interpretable manner which allows for the investigation of the
presence of spatial variability in the determinants of mortality. Third, techniques for
updating traditional mortality models are introduced, culminating in the development
of a model which addresses the relationship between space, economic growth, and
mortality. It is through these applications that this dissertation demonstrates the
utility in updating actuarial mortality models from a spatial perspective.