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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


This dissertation involves three problems that are all related by the use of the singular value decomposition (SVD) or generalized singular value decomposition (GSVD). The specific problems are (i) derivation of a generalized singular value expansion (GSVE), (ii) analysis of the properties of the chi-squared method for regularization parameter selection in the case of nonnormal data and (iii) formulation of a partial canonical correlation concept for continuous time stochastic processes. The finite dimensional SVD has an infinite dimensional generalization to compact operators. However, the form of the finite dimensional GSVD developed in, e.g., Van Loan does not extend directly to …

Contributors
Huang, Qing, Eubank, Randall, Renaut, Rosemary, et al.
Created Date
2012

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a …

Contributors
Baydogan, Mustafa Gokce, Runger, George C, Atkinson, Robert, et al.
Created Date
2012

Estimating cointegrating relationships requires specific techniques. Canonical correlations are used to determine the rank and space of the cointegrating matrix. The vectors used to transform the data into canonical variables have an eigenvector representation, and the associated canonical correlations have an eigenvalue representation. The number of cointegrating relations is chosen based upon a theoretical difference in the convergence rates of the eignevalues. The number of cointegrating relations is consistently estimated using a threshold function which places a lower bound on the eigenvalues associated with cointegrating relations and an upper bound on the eigenvalues on the eigenvalues not associated with cointegrating …

Contributors
Nowak, Adam Daniel, Ahn, Seung C, Liu, Crocker, et al.
Created Date
2012

With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied …

Contributors
Koh, Derek, Runger, George, Wu, Tong, et al.
Created Date
2013

The use of bias indicators in psychological measurement has been contentious, with some researchers questioning whether they actually suppress or moderate the ability of substantive psychological indictors to discriminate (McGrath, Mitchell, Kim, & Hough, 2010). Bias indicators on the MMPI-2-RF (F-r, Fs, FBS-r, K-r, and L-r) were tested for suppression or moderation of the ability of the RC1 and NUC scales to discriminate between Epileptic Seizures (ES) and Non-epileptic Seizures (NES, a conversion disorder that is often misdiagnosed as ES). RC1 and NUC had previously been found to be the best scales on the MMPI-2-RF to differentiate between ES and …

Contributors
Wershba, Rebecca Eve, Lanyon, Richard I, Barrera, Manuel, et al.
Created Date
2013

Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was …

Contributors
Fralick, Celeste Rachelle, Muthuswamy, Jitendran, O'Shea, Terrance, et al.
Created Date
2013

Parallel Monte Carlo applications require the pseudorandom numbers used on each processor to be independent in a probabilistic sense. The TestU01 software package is the standard testing suite for detecting stream dependence and other properties that make certain pseudorandom generators ineffective in parallel (as well as serial) settings. TestU01 employs two basic schemes for testing parallel generated streams. The first applies serial tests to the individual streams and then tests the resulting P-values for uniformity. The second turns all the parallel generated streams into one long vector and then applies serial tests to the resulting concatenated stream. Various forms of …

Contributors
Ismay, Chester Ivan, Eubank, Randall, Young, Dennis, et al.
Created Date
2013

Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of …

Contributors
Reichenberg, Ray E., Levy, Roy, Thompson, Marilyn S., et al.
Created Date
2013

This work presents two complementary studies that propose heuristic methods to capture characteristics of data using the ensemble learning method of random forest. The first study is motivated by the problem in education of determining teacher effectiveness in student achievement. Value-added models (VAMs), constructed as linear mixed models, use students’ test scores as outcome variables and teachers’ contributions as random effects to ascribe changes in student performance to the teachers who have taught them. The VAMs teacher score is the empirical best linear unbiased predictor (EBLUP). This approach is limited by the adequacy of the assumed model specification with respect …

Contributors
Valdivia, Arturo, Eubank, Randall, Young, Dennis, et al.
Created Date
2013

Many longitudinal studies, especially in clinical trials, suffer from missing data issues. Most estimation procedures assume that the missing values are ignorable or missing at random (MAR). However, this assumption leads to unrealistic simplification and is implausible for many cases. For example, an investigator is examining the effect of treatment on depression. Subjects are scheduled with doctors on a regular basis and asked questions about recent emotional situations. Patients who are experiencing severe depression are more likely to miss an appointment and leave the data missing for that particular visit. Data that are not missing at random may produce bias …

Contributors
Zhang, Jun, Reiser, Mark, Barber, Jarrett, et al.
Created Date
2013