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 firstname.lastname@example.org.
- 4 English
- 4 Public
- 2 Industrial engineering
- 1 Binomial Responses
- 1 D-optimality
- 1 Design of Experiment
- 1 Experimental Design
- 1 GLM
- 1 GLMM
- 1 Industrial Engineering
- 1 Mixture Experiments
- 1 Mixture-Process Experiments
- 1 Multinomial Responses
- 1 Optimal Design
- 1 Optimal Designs
- 1 Ordinal Data
- 1 Pareto Front
- 1 Random Block
- 1 Random Intercept
- 1 Response Surface Methodology
- 1 Robust Parameter Design
- 1 Split-Plot Design
- 1 dual-response
- 1 optimal design
Optimal experimental design for generalized linear models is often done using a pseudo-Bayesian approach that integrates the design criterion across a prior distribution on the parameter values. This approach ignores the lack of utility of certain models contained in the prior, and a case is demonstrated where the heavy focus on such hopeless models results in a design with poor performance and with wild swings in coverage probabilities for Wald-type confidence intervals. Design construction using a utility-based approach is shown to result in much more stable coverage probabilities in the area of greatest concern. The pseudo-Bayesian approach can be applied …
- Hassler, Edgar, Montgomery, Douglas C, Silvestrini, Rachel T, et al.
- Created Date
Mixture experiments are useful when the interest is in determining how changes in the proportion of an experimental component affects the response. This research focuses on the modeling and design of mixture experiments when the response is categorical namely, binary and ordinal. Data from mixture experiments is characterized by the perfect collinearity of the experimental components, resulting in model matrices that are singular and inestimable under likelihood estimation procedures. To alleviate problems with estimation, this research proposes the reparameterization of two nonlinear models for ordinal data -- the proportional-odds model with a logistic link and the stereotype model. A study …
- Mancenido, Michelle V., Montgomery, Douglas C, Pan, Rong, et al.
- Created Date
In mixture-process variable experiments, it is common that the number of runs is greater than in mixture-only or process-variable experiments. These experiments have to estimate the parameters from the mixture components, process variables, and interactions of both variables. In some of these experiments there are variables that are hard to change or cannot be controlled under normal operating conditions. These situations often prohibit a complete randomization for the experimental runs due to practical and economical considerations. Furthermore, the process variables can be categorized into two types: variables that are controllable and directly affect the response, and variables that are uncontrollable …
- Cho, Tae-Yeon, Montgomery, Douglas C, Borror, Connie M, et al.
- Created Date
The majority of research in experimental design has, to date, been focused on designs when there is only one type of response variable under consideration. In a decision-making process, however, relying on only one objective or criterion can lead to oversimplified, sub-optimal decisions that ignore important considerations. Incorporating multiple, and likely competing, objectives is critical during the decision-making process in order to balance the tradeoffs of all potential solutions. Consequently, the problem of constructing a design for an experiment when multiple types of responses are of interest does not have a clear answer, particularly when the response variables have different …
- Burke, Sarah Ellen, Montgomery, Douglas C, Borror, Connie M, et al.
- Created Date