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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- All Subjects: Statistics
- Creators: Reiser, Mark
Description
Longitudinal data involving multiple subjects is quite popular in medical and social science areas. I consider generalized linear mixed models (GLMMs) applied to such longitudinal data, and the optimal design searching problem under such models. In this case, based on optimal design theory, the optimality criteria depend on the estimated parameters, which leads to local optimality. Moreover, the information matrix under a GLMM doesn't have a closed-form expression. My dissertation includes three topics related to this design problem. The first part is searching for locally optimal designs under GLMMs with longitudinal data. I apply penalized quasi-likelihood (PQL) method to approximate the information matrix and compare several approximations to show the superiority of PQL over other approximations. Under different local parameters and design restrictions, locally D- and A- optimal designs are constructed based on the approximation. An interesting finding is that locally optimal designs sometimes apply different designs to different subjects. Finally, the robustness of these locally optimal designs is discussed. In the second part, an unknown observational covariate is added to the previous model. With an unknown observational variable in the experiment, expected optimality criteria are considered. Under different assumptions of the unknown variable and parameter settings, locally optimal designs are constructed and discussed. In the last part, Bayesian optimal designs are considered under logistic mixed models. Considering different priors of the local parameters, Bayesian optimal designs are generated. Bayesian design under such a model is usually expensive in time. The running time in this dissertation is optimized to an acceptable amount with accurate results. I also discuss the robustness of these Bayesian optimal designs, which is the motivation of applying such an approach.
ContributorsShi, Yao (Author) / Stufken, John (Thesis advisor) / Kao, Ming-Hung (Thesis advisor) / Lan, Shiwei (Committee member) / Pan, Rong (Committee member) / Reiser, Mark (Committee member) / Arizona State University (Publisher)
Created2022
Description
Humans cooperate at levels unseen in other species. Identifying the adaptive mechanisms driving this unusual behavior, as well as how these mechanisms interact to create complex cooperative patterns, remains an open question in anthropology. One impediment to such investigations is that complete, long-term datasets of human cooperative behaviors in small-scale societies are hard to come by; such field research is often hindered both by humans' long lifespans and by the difficulties of collecting data in remote societies. In this study, I attempted to overcome these methodological challenges by simulating individual human cooperative behaviors in a small-scale population. Using an agent-based model tuned to population-level measurements from a real-life marine subsistence population in the southern Philippines, I generated dynamic daily cooperative behaviors in a hypothetical subsistence population over a period of 1500 years and 42 overlapping generations. Preliminary findings from the model suggest that, while the agent-based model broadly captured a number of characteristic population-level patterns in the subsistence population, it did not fully replicate nuances of the population's observed cooperative behaviors. In particular, statistical models of the simulated data identified reciprocity-based and need-based cooperative behaviors but did not detect kinship-motivated cooperation, despite the fact that kin cooperation traits evolved positively and reciprocity cooperation traits evolved negatively over time in the agent population. It is possible that this discrepancy reflects a complex interaction between kinship and reciprocity in the agent-based model. On the other hand, it may also suggest that these types of statistical models, which are frequently utilized in human cooperation studies in the anthropological literature, do not reliably discriminate between kin-based and reciprocity-based cooperation mechanisms when both exist in a population. Even so, the completeness of the simulated data enabled use of more complex statistical methodologies which were able to disentangle the relative effects of cooperative mechanisms operating at different decision levels. By addressing remaining pattern-matching issues, future iterations of the agent-based model may prove to be a useful tool for validating empirical research and investigating novel hypotheses about the evolution and maintenance of cooperative behaviors in human populations.
ContributorsPhelps, Julia R. (Author) / Reiser, Mark (Thesis advisor) / Saul, Steven (Thesis advisor) / Morgan, Thomas (Committee member) / Arizona State University (Publisher)
Created2023
Description
Tracking disease cases is an essential task in public health; however, tracking the number of cases of a disease may be difficult not every infection can be recorded by public health authorities. Notably, this may happen with whole country measles case reports, even such countries with robust registration systems. Eilertson et al. (2019) propose using a state-space model combined with maximum likelihood methods for estimating measles transmission. A Bayesian approach that uses particle Markov Chain Monte Carlo (pMCMC) is proposed to estimate the parameters of the non-linear state-space model developed in Eilertson et al. (2019) and similar previous studies. This dissertation illustrates the performance of this approach by calculating posterior estimates of the model parameters and predictions of the unobserved states in simulations and case studies. Also, Iteration Filtering (IF2) is used as a support method to verify the Bayesian estimation and to inform the selection of prior distributions. In the second half of the thesis, a birth-death process is proposed to model the unobserved population size of a disease vector. This model studies the effect of a disease vector population size on a second affected population. The second population follows a non-homogenous Poisson process when conditioned on the vector process with a transition rate given by a scaled version of the vector population. The observation model also measures a potential threshold event when the host species population size surpasses a certain level yielding a higher transmission rate. A maximum likelihood procedure is developed for this model, which combines particle filtering with the Minorize-Maximization (MM) algorithm and extends the work of Crawford et al. (2014).
ContributorsMartinez Rivera, Wilmer Osvaldo (Author) / Fricks, John (Thesis advisor) / Reiser, Mark (Committee member) / Zhou, Shuang (Committee member) / Cheng, Dan (Committee member) / Lan, Shiwei (Committee member) / Arizona State University (Publisher)
Created2022
Description
Goodness-of-fit test is a hypothesis test used to test whether a given model fit the data well. It is extremely difficult to find a universal goodness-of-fit test that can test all types of statistical models. Moreover, traditional Pearson’s chi-square goodness-of-fit test is sometimes considered to be an omnibus test but not a directional test so it is hard to find the source of poor fit when the null hypothesis is rejected and it will lose its validity and effectiveness in some of the special conditions. Sparseness is such an abnormal condition. One effective way to overcome the adverse effects of sparseness is to use limited-information statistics. In this dissertation, two topics about constructing and using limited-information statistics to overcome sparseness for binary data will be included. In the first topic, the theoretical framework of pairwise concordance and the transformation matrix which is used to extract the corresponding marginals and their generalizations are provided. Then a series of new chi-square test statistics and corresponding orthogonal components are proposed, which are used to detect the model misspecification for longitudinal binary data. One of the important conclusions is, the test statistic $X^2_{2c}$ can be taken as an extension of $X^2_{[2]}$, the second-order marginals of traditional Pearson’s chi-square statistic. In the second topic, the research interest is to investigate the effect caused by different intercept patterns when using Lagrange multiplier (LM) test to find the source of misfit for two items in 2-PL IRT model. Several other directional chi-square test statistics are taken into comparison. The simulation results showed that the intercept pattern does affect the performance of goodness-of-fit test, especially the power to find the source of misfit if the source of misfit does exist. More specifically, the power is directly affected by the `intercept distance' between two misfit variables. Another discovery is, the LM test statistic has the best balance between the accurate Type I error rates and high empirical power, which indicates the LM test is a robust test.
ContributorsXu, Jinhui (Author) / Reiser, Mark (Thesis advisor) / Kao, Ming-Hung (Committee member) / Wilson, Jeffrey (Committee member) / Zheng, Yi (Committee member) / Edwards, Michael (Committee member) / Arizona State University (Publisher)
Created2022
Description
Inside cells, axonal and dendritic transport by motor proteins is a process that is responsible for supplying cargo, such as vesicles and organelles, to support neuronal function. Motor proteins achieve transport through a cycle of chemical and mechanical processes. Particle tracking experiments are used to study this intracellular cargo transport by recording multi-dimensional, discrete cargo position trajectories over time. However, due to experimental limitations, much of the mechanochemical process cannot be directly observed, making mathematical modeling and statistical inference an essential tool for identifying the underlying mechanisms. The cargo movement during transport is modeled using a switching stochastic differential equation framework that involves classification into one of three proposed hidden regimes. Each regime is characterized by different levels of velocity and stochasticity. The equations are presented as a state-space model with Markovian properties. Through a stochastic expectation-maximization algorithm, statistical inference can be made based on the observed trajectory. Regime predictions and particle location predictions are calculated through an auxiliary particle filter and particle smoother. Based on these predictions, parameters are estimated through maximum likelihood. Diagnostics are proposed that can assess model performance and therefore also be a form of model selection criteria. Model selection is used to find the most accurate regime models and the optimal number of regimes for a certain motor-cargo system. A method for incorporating a second positional dimension is also introduced. These methods are tested on both simulated data and different types of experimental data.
ContributorsCrow, Lauren (Author) / Fricks, John (Thesis advisor) / McKinley, Scott (Committee member) / Hahn, Paul R (Committee member) / Reiser, Mark (Committee member) / Cheng, Dan (Committee member) / Arizona State University (Publisher)
Created2021