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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.


Subject
Date Range
2010 2019


Electricity infrastructure vulnerabilities were assessed for future heat waves due to climate change. Critical processes and component relationships were identified and characterized with consideration for the terminal event of service outages, including cascading failures in transmission-level components that can result in blackouts. The most critical dependency identified was the increase in peak electricity demand with higher air temperatures. Historical and future air temperatures were characterized within and across Los Angeles County, California (LAC) and Maricopa County (Phoenix), Arizona. LAC was identified as more vulnerable to heat waves than Phoenix due to a wider distribution of historical temperatures. Two approaches were …

Contributors
Burillo, Daniel, Chester, Mikhail V, Ruddell, Benjamin, et al.
Created Date
2018

Large-scale cultivation of perennial bioenergy crops (e.g., miscanthus and switch- 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 …

Contributors
Wang, Meng, Kamarianakis, Yiannis, Georgescu, Matei, et al.
Created Date
2018

Transfer learning is a sub-field of statistical modeling and machine learning. It refers to methods that integrate the knowledge of other domains (called source domains) and the data of the target domain in a mathematically rigorous and intelligent way, to develop a better model for the target domain than a model using the data of the target domain alone. While transfer learning is a promising approach in various application domains, my dissertation research focuses on the particular application in health care, including telemonitoring of Parkinson’s Disease (PD) and radiomics for glioblastoma. The first topic is a Mixed Effects Transfer Learning …

Contributors
Yoon, Hyunsoo, Li, Jing, Wu, Teresa, et al.
Created Date
2018

Statistical model selection using the Akaike Information Criterion (AIC) and similar criteria is a useful tool for comparing multiple and non-nested models without the specification of a null model, which has made it increasingly popular in the natural and social sciences. De- spite their common usage, model selection methods are not driven by a notion of statistical confidence, so their results entail an unknown de- gree of uncertainty. This paper introduces a general framework which extends notions of Type-I and Type-II error to model selection. A theo- retical method for controlling Type-I error using Difference of Goodness of Fit (DGOF) …

Contributors
Cullan, Michael, Sterner, Beckett, Fricks, John, et al.
Created Date
2018

Due to large data resources generated by online educational applications, Educational Data Mining (EDM) has improved learning effects in different ways: Students Visualization, Recommendations for students, Students Modeling, Grouping Students, etc. A lot of programming assignments have the features like automating submissions, examining the test cases to verify the correctness, but limited studies compared different statistical techniques with latest frameworks, and interpreted models in a unified approach. In this thesis, several data mining algorithms have been applied to analyze students’ code assignment submission data from a real classroom study. The goal of this work is to explore and predict students’ …

Contributors
Tian, Wenbo, Hsiao, Ihan, Bazzi, Rida, et al.
Created Date
2019

In this work, I present a Bayesian inference computational framework for the analysis of widefield microscopy data that addresses three challenges: (1) counting and localizing stationary fluorescent molecules; (2) inferring a spatially-dependent effective fluorescence profile that describes the spatially-varying rate at which fluorescent molecules emit subsequently-detected photons (due to different illumination intensities or different local environments); and (3) inferring the camera gain. My general theoretical framework utilizes the Bayesian nonparametric Gaussian and beta-Bernoulli processes with a Markov chain Monte Carlo sampling scheme, which I further specify and implement for Total Internal Reflection Fluorescence (TIRF) microscopy data, benchmarking the method on …

Contributors
Wallgren, Ross Tod, Presse, Steve, Armbruster, Hans, et al.
Created Date
2019

A simulation study was conducted to explore the robustness of general factor mean difference estimation in bifactor ordered-categorical data. In the No Differential Item Functioning (DIF) conditions, the data generation conditions varied were sample size, the number of categories per item, effect size of the general factor mean difference, and the size of specific factor loadings; in data analysis, misspecification conditions were introduced in which the generated bifactor data were fit using a unidimensional model, and/or ordered-categorical data were treated as continuous data. In the DIF conditions, the data generation conditions varied were sample size, the number of categories per …

Contributors
Liu, Yixing, Thompson, Marilyn, Levy, Roy, et al.
Created Date
2019

The concept of distribution is one of the core ideas of probability theory and inferential statistics, if not the core idea. Many introductory statistics textbooks pay lip service to stochastic/random processes but how do students think about these processes? This study sought to explore what understandings of stochastic process students develop as they work through materials intended to support them in constructing the long-run behavior meaning for distribution. I collected data in three phases. First, I conducted a set of task-based clinical interviews that allowed me to build initial models for the students’ meanings for randomness and probability. Second, I …

Contributors
Hatfield, Neil, Thompson, Patrick, Carlson, Marilyn, et al.
Created Date
2019

This thesis presents a family of adaptive curvature methods for gradient-based stochastic optimization. In particular, a general algorithmic framework is introduced along with a practical implementation that yields an efficient, adaptive curvature gradient descent algorithm. To this end, a theoretical and practical link between curvature matrix estimation and shrinkage methods for covariance matrices is established. The use of shrinkage improves estimation accuracy of the curvature matrix when data samples are scarce. This thesis also introduce several insights that result in data- and computation-efficient update equations. Empirical results suggest that the proposed method compares favorably with existing second-order techniques based on …

Contributors
Barron, Trevor Paul, Ben Amor, Heni, He, Jingrui, et al.
Created Date
2019

Bayesian Additive Regression Trees (BART) is a non-parametric Bayesian model that often outperforms other popular predictive models in terms of out-of-sample error. This thesis studies a modified version of BART called Accelerated Bayesian Additive Regression Trees (XBART). The study consists of simulation and real data experiments comparing XBART to other leading algorithms, including BART. The results show that XBART maintains BART’s predictive power while reducing its computation time. The thesis also describes the development of a Python package implementing XBART. Dissertation/Thesis

Contributors
Yalov, Saar, Hahn, P. Richard, McCulloch, Robert, et al.
Created Date
2019