ETDs

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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A Comparison of Two Approaches for Constructing Exact Designs for Mixed Binary and Continuous Responses.

Description
This thesis is concerned with experimental designs for studies a controllable independent variable X, a continuous response variable Y and a binary response variable Z. It is known that judiciously selected design allows experimenters to collect informative data for making precise and valid statistical inferences with minimum cost. However, for…

This thesis is concerned with experimental designs for studies a controllable independent variable X, a continuous response variable Y and a binary response variable Z. It is known that judiciously selected design allows experimenters to collect informative data for making precise and valid statistical inferences with minimum cost. However, for the complex set- ting that this thesis consider, designs that yield a high expected estimation precision may still possess a high probability of having non-estimable parameters, especially when the sample size is small. Such an observation has been reported in some previous works on the separation issue for, e.g., the logistic regression. Therefore, when selecting a study design, it is important to consider both the expected variances of the parameter estimates, and the probability for having non-estimable parameters.A comparison of two approaches for constructing designs for the previously mentioned setting with a mixed responses model is presented in this work. The two design approaches are the locally A-optimal design approach, and a penalized A-optimal design approach that involves the optimization of A-optimality criterion plus the penalty term to reduce the chance of including designs points that have a high probability to make some parameters non-estimable.
ContributorsAbdullah, Bayan Abdulaziz (Author) / Kao, Ming-Hung MK (Thesis advisor) / Cheng, Dan DC (Committee member) / Zheng, Yi YZ (Committee member) / Arizona State University (Publisher)
Created2021
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Statistical Inference for Multiple Change Points and Implicit Network Structures

Description
This dissertation contains two research projects: Multiple Change Point Detection in Linear Models and Statistical Inference for Implicit Network Structures. In the first project, a new method to detect the number and locations of change points in piecewise linear models under stationary Gaussian noise is proposed. The method transforms the problem…

This dissertation contains two research projects: Multiple Change Point Detection in Linear Models and Statistical Inference for Implicit Network Structures. In the first project, a new method to detect the number and locations of change points in piecewise linear models under stationary Gaussian noise is proposed. The method transforms the problem of detecting change points to the detection of local extrema by kernel smoothing and differentiating the data sequence. The change points are detected by computing the p-values for all local extrema using the derived peak height distributions of smooth Gaussian processes, and then applying the Benjamini-Hochberg procedure to identify significant local extrema. Theoretical results show that the method can guarantee asymptotic control of the False Discover Rate (FDR) and power consistency, as the length of the sequence, and the size of slope changes and jumps get large. In addition, compared to traditional methods for change point detection based on recursive segmentation, The proposed method tests the candidate local extrema only one time, achieving the smallest computational complexity. Numerical studies show that the properties on FDR control and power consistency are maintained in non-asymptotic cases. In the second project, identifiability and estimation consistency under mild conditions in hub model are proved. Hub Model is a model-based approach, introduced by Zhao and Weko (2019), to infer implicit network structuress from grouping behavior. The hub model assumes that each member of the group is brought together by a member of the group called the hub. This paper generalize the hub model by introducing a model component that allows hubless groups in which individual nodes spontaneously appear independent of any other individual. The new model bridges the gap between the hub model and the degenerate case of the mixture model -- the Bernoulli product. Furthermore, a penalized likelihood approach is proposed to estimate the set of hubs when it is unknown.
ContributorsHe, Zhibing (Author) / Zhao, Yunpeng YZ (Thesis advisor) / Cheng, Dan DC (Thesis advisor) / Lopes, Hedibert HL (Committee member) / Fricks, John JF (Committee member) / Kao, Ming-Hung MK (Committee member) / Arizona State University (Publisher)
Created2022