Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
This dissertation explores applications of machine learning methods in service of the design of screening tests, which are ubiquitous in applications from social work, to criminology, to healthcare. In the first part, a novel Bayesian decision theory framework is presented for designing tree-based adaptive tests. On an application to youth delinquency in Honduras, the method produces a 15-item instrument that is almost as accurate as a full-length 150+ item test. The framework includes specific considerations for the context in which the test will be administered, and provides uncertainty quantification around the trade-offs of shortening lengthy tests. In the second part, classification complexity is explored via theoretical and empirical results from statistical learning theory, information theory, and empirical data complexity measures. A simulation study that explicitly controls two key aspects of classification complexity is performed to relate the theoretical and empirical approaches. Throughout, a unified language and notation that formalizes classification complexity is developed; this same notation is used in subsequent chapters to discuss classification complexity in the context of a speech-based screening test. In the final part, the relative merits of task and feature engineering when designing a speech-based cognitive screening test are explored. Through an extensive classification analysis on a clinical speech dataset from patients with normal cognition and Alzheimer’s disease, the speech elicitation task is shown to have a large impact on test accuracy; carefully performed task and feature engineering are required for best results. A new framework for objectively quantifying speech elicitation tasks is introduced, and two methods are proposed for automatically extracting insights into the aspects of the speech elicitation task that are driving classification performance. The dissertation closes with recommendations for how to evaluate the obtained insights and use them to guide future design of speech-based screening tests.
ContributorsKrantsevich, Chelsea (Author) / Hahn, P. Richard (Thesis advisor) / Berisha, Visar (Committee member) / Lopes, Hedibert (Committee member) / Renaut, Rosemary (Committee member) / Zheng, Yi (Committee member) / Arizona State University (Publisher)
Created2023
Description
Eigenvalues of the Gram matrix formed from received data frequently appear in sufficient detection statistics for multi-channel detection with Generalized Likelihood Ratio (GLRT) and Bayesian tests. In a frequently presented model for passive radar, in which the null hypothesis is that the channels are independent and contain only complex white Gaussian noise and the alternative hypothesis is that the channels contain a common rank-one signal in the mean, the GLRT statistic is the largest eigenvalue $\lambda_1$ of the Gram matrix formed from data. This Gram matrix has a Wishart distribution. Although exact expressions for the distribution of $\lambda_1$ are known under both hypotheses, numerically calculating values of these distribution functions presents difficulties in cases where the dimension of the data vectors is large. This dissertation presents tractable methods for computing the distribution of $\lambda_1$ under both the null and alternative hypotheses through a technique of expanding known expressions for the distribution of $\lambda_1$ as inner products of orthogonal polynomials. These newly presented expressions for the distribution allow for computation of detection thresholds and receiver operating characteristic curves to arbitrary precision in floating point arithmetic. This represents a significant advancement over the state of the art in a problem that could previously only be addressed by Monte Carlo methods.
ContributorsJones, Scott, Ph.D (Author) / Cochran, Douglas (Thesis advisor) / Berisha, Visar (Committee member) / Bliss, Daniel (Committee member) / Kosut, Oliver (Committee member) / Richmond, Christ (Committee member) / Arizona State University (Publisher)
Created2019
Description
Land-use change has arguably been the largest contributor to the emergence of novel zoonotic diseases within the past century. However, the relationship between patterns of land-use change and the resulting landscape configuration on disease spread is poorly understood as current cross-species disease transmission models have not adequately incorporated spatial features of habitats. Furthermore, mathematical-epidemiological studies have not considered the role that land-use change plays in disease transmission throughout an ecosystem.
This dissertation models how a landscape's configuration, examining the amount and shape of habitat overlap, contributes to cross-species disease transmission to determine the role that land-use change has on the spread of infectious diseases. To approach this, an epidemiological model of transmission between a domesticated and a wild species is constructed. Each species is homogeneously mixed in its respective habitat and heterogeneously mixed in the habitat overlap, where cross-species transmission occurs. Habitat overlap is modeled using landscape ecology metrics.
This general framework is then applied to brucellosis transmission between elk and cattle in the Greater Yellowstone Ecosystem. The application of the general framework allows for the exploration of how land-use change has contributed to brucellosis prevalence in these two species, and how land management can be utilized to control disease transmission. This model is then extended to include a third species, bison, in order to provide insight to the indirect consequences of disease transmission for a species that is situated on land that has not been converted. The results of this study can ultimately help stakeholders develop policy for controlling brucellosis transmission between livestock, elk, and bison, and in turn, could lead to less disease prevalence, reduce associated costs, and assist in population management.
This research contributes novelty by combining landscape ecology metrics with theoretical epidemiological models to understand how the shape, size, and distribution of habitat fragments on a landscape affect cross-species disease transmission. The general framework demonstrates how habitat edge in single patch impacts cross-species disease transmission. The application to brucellosis transmission in the Greater Yellowstone Ecosystem between elk, cattle, and bison is original research that enhances understanding of how land conversion is associated with enzootic disease spread.
This dissertation models how a landscape's configuration, examining the amount and shape of habitat overlap, contributes to cross-species disease transmission to determine the role that land-use change has on the spread of infectious diseases. To approach this, an epidemiological model of transmission between a domesticated and a wild species is constructed. Each species is homogeneously mixed in its respective habitat and heterogeneously mixed in the habitat overlap, where cross-species transmission occurs. Habitat overlap is modeled using landscape ecology metrics.
This general framework is then applied to brucellosis transmission between elk and cattle in the Greater Yellowstone Ecosystem. The application of the general framework allows for the exploration of how land-use change has contributed to brucellosis prevalence in these two species, and how land management can be utilized to control disease transmission. This model is then extended to include a third species, bison, in order to provide insight to the indirect consequences of disease transmission for a species that is situated on land that has not been converted. The results of this study can ultimately help stakeholders develop policy for controlling brucellosis transmission between livestock, elk, and bison, and in turn, could lead to less disease prevalence, reduce associated costs, and assist in population management.
This research contributes novelty by combining landscape ecology metrics with theoretical epidemiological models to understand how the shape, size, and distribution of habitat fragments on a landscape affect cross-species disease transmission. The general framework demonstrates how habitat edge in single patch impacts cross-species disease transmission. The application to brucellosis transmission in the Greater Yellowstone Ecosystem between elk, cattle, and bison is original research that enhances understanding of how land conversion is associated with enzootic disease spread.
ContributorsPadilla, Dustin (Author) / Perrings, Charles (Thesis advisor) / Brauer, Fred (Committee member) / Jayasuriya, Suren (Committee member) / Arizona State University (Publisher)
Created2020