## 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.

- Reiser, Mark
- 31 Arizona State University
- 19 Kao, Ming-Hung
- 11 Stufken, John
- 7 Zheng, Yi
- 5 Kamarianakis, Ioannis
- 5 Pan, Rong
- more
- 5 Wilson, Jeffrey
- 4 Eubank, Randall
- 4 Kamarianakis, Yiannis
- 4 Wilson, Jeffrey R
- 4 Young, Dennis
- 3 Barber, Jarrett
- 3 Lanchier, Nicolas
- 3 McCulloch, Robert
- 2 Broatch, Jennifer
- 2 Cheng, Dan
- 2 Fricks, John
- 2 St Louis, Robert
- 2 Yang, Yan
- 1 Aiken, Leona S
- 1 Alghamdi, Reem
- 1 Alrumayh, Amani
- 1 Armbruster, Dieter
- 1 Buscaglia, Robert
- 1 Carlson, Marilyn
- 1 Coxe, Stefany Jean
- 1 Cupido, Kyran
- 1 Dassanayake, Mudiyanselage Maduranga Kasun
- 1 Dueck, Amylou
- 1 Fotheringham, A. Stewart
- 1 Fotheringham, Stewart
- 1 Georgescu, Matei
- 1 Giacomazzo, Mario
- 1 Hahn, Paul R
- 1 Hahn, Richard
- 1 Hatfield, Neil
- 1 Huang, Ping-Chieh
- 1 Irimata, Katherine
- 1 Irimata, Kyle
- 1 Ismay, Chester Ivan
- 1 Jevtic, Petar
- 1 Kadell, Kevin W. J.
- 1 Kao, Jason
- 1 Kao, Ming-hung
- 1 Khogeer, Hazar Abdulrahman
- 1 Kim, Soohyun
- 1 Lanchier, Nicholas
- 1 Lehrer, Richard
- 1 Li, Jingjin
- 1 Lohr, Sharon
- 1 Mackinnon, David P
- 1 Manley, Michael
- 1 Middleton, James
- 1 Milovanovic, Jelena
- 1 Montgomery, Douglas C
- 1 Moustaoui, Mohamed
- 1 Paez, Antonio
- 1 Rha, Hyungmin
- 1 St Louis, Robert D
- 1 St Louis, Robert D.
- 1 St. Louis, Robert
- 1 Temkit, M'Hamed
- 1 Thompson, Patrick
- 1 Valdivia, Arturo
- 1 Vazquez Arreola, Elsa Aimara
- 1 Wang, Bei
- 1 Wang, Meng
- 1 Wang, Zhongshen
- 1 Welfert, Bruno
- 1 West, Stephen G
- 1 Wilson, Jeffrey Wilson
- 1 Yan, Hao
- 1 Yin, Jianqiong
- 1 Yu, Wanchunzi
- 1 Zhang, Jun
- 1 Zhou, Lin
- 1 Zhu, Junfei
- 1 van Schaijik, Maria

- application/pdf
- 1 text/plain

- 31 Public

- Statistics
- 2 Biostatistics
- 2 Computer science
- 2 Mathematics
- 2 equivalence theorem
- 1 Applied mathematics
- 1 Asymptotic Power
- more
- 1 Bayesian Shrinkage
- 1 Big Data
- 1 Binary and continuous responses
- 1 Binary response
- 1 Bootstrap
- 1 Bootstrap aggregating
- 1 Bootstrapping
- 1 Chi-Square goodness-of-fit tests
- 1 Classification
- 1 Complete class
- 1 Confidence Set
- 1 Correlation
- 1 D-optimality
- 1 Data Mining
- 1 Degrees of freedom
- 1 Ensemble Learning
- 1 Functional Data Analysis
- 1 Functional data analysis
- 1 Functional principal component analysis
- 1 Functional regression models
- 1 GLMs
- 1 Generalized Method of Moments
- 1 Generalized linear mixed model
- 1 Generalized linear model
- 1 Hierarchical Data
- 1 Hierarchical data
- 1 IBOSS
- 1 Interactions
- 1 Item Response Model
- 1 Lasso
- 1 Locally optimal design
- 1 Locally optimal designs
- 1 Logistic Regression
- 1 Lupus
- 1 Mathematics education
- 1 Mixed Models
- 1 Mixed model
- 1 Model Selection
- 1 Nonlinear Time Series
- 1 OLS
- 1 Optimal sampling schedule
- 1 Orthogonal Components
- 1 Orthogonal components of chi-square statistic
- 1 PSO
- 1 Power Analysis
- 1 Quadratic Growth Curves
- 1 Quantitative psychology and psychometrics
- 1 Random Effects
- 1 Random Forest
- 1 Reduced Major Axis
- 1 Regression
- 1 Robust designs
- 1 Rresampling schemes
- 1 SETAR
- 1 Small sample
- 1 Small sample inferences
- 1 Sparseness
- 1 Statistical Learning
- 1 Subdata Selection
- 1 Supervised Learning
- 1 Threshold Regression
- 1 Value Added Models
- 1 Variable Importance
- 1 Variable Selection
- 1 Vertex
- 1 complete class
- 1 correlated data
- 1 decomposition of chi-square statistic
- 1 estimating equations
- 1 fMRI
- 1 generalized linear models
- 1 generalized method of moments
- 1 goodness-of-fit
- 1 locally optimal design
- 1 locally optimal designs
- 1 logistic models
- 1 logistic regression models
- 1 longitudinal data
- 1 mathematics education
- 1 multivariate models
- 1 orthogonal arrays
- 1 parallel computing
- 1 pseudorandom numbers
- 1 spectral clustering
- 1 statistics education
- 1 testing of generators
- 1 tests of vector independence
- 1 time dependency
- 1 valid moment conditions

- Reverse Fountain Cytoplasmic Streaming in Rhizopus Oryzae
- Diné Research Practices and Protocols: An Intersectional Paradigm Incorporating Indigenous Feminism, Critical Indigenous Research Methodologies and Diné Knowledge Systems
- Developing an Augmented Reality Solution for Mapping Underground Infrastructure
- Characterization of 2D Human Ankle Stiffness during Postural Balance and Walking for Robot-aided Ankle Rehabilitation
- Using Molecular, Cellular and Bioengineering Approaches Towards Understanding Muscle Stem Cell Biology

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

The Pearson and likelihood ratio statistics are commonly used to test goodness-of-fit for models applied to data from a multinomial distribution. When data are from a table formed by cross-classification of a large number of variables, the common statistics may have low power and inaccurate Type I error level due to sparseness in the cells of the table. The GFfit statistic can be used to examine model fit in subtables. It is proposed to assess model fit by using a new version of GFfit statistic based on orthogonal components of Pearson chi-square as a diagnostic to examine the fit on …

- Contributors
- Zhu, Junfei, Reiser, Mark, Stufken, John, et al.
- Created Date
- 2017

The Pearson and likelihood ratio statistics are well-known in goodness-of-fit testing and are commonly used for models applied to multinomial count data. When data are from a table formed by the cross-classification of a large number of variables, these goodness-of-fit statistics may have lower power and inaccurate Type I error rate due to sparseness. Pearson's statistic can be decomposed into orthogonal components associated with the marginal distributions of observed variables, and an omnibus fit statistic can be obtained as a sum of these components. When the statistic is a sum of components for lower-order marginals, it has good performance for …

- Contributors
- Dassanayake, Mudiyanselage Maduranga Kasun, Reiser, Mark, Kao, Ming-Hung, et al.
- Created Date
- 2018

Quadratic growth curves of 2nd degree polynomial are widely used in longitudinal studies. For a 2nd degree polynomial, the vertex represents the location of the curve in the XY plane. For a quadratic growth curve, we propose an approximate confidence region as well as the confidence interval for x and y-coordinates of the vertex using two methods, the gradient method and the delta method. Under some models, an indirect test on the location of the curve can be based on the intercept and slope parameters, but in other models, a direct test on the vertex is required. We present a …

- Contributors
- Yu, Wanchunzi, Reiser, Mark, Barber, Jarrett, et al.
- Created Date
- 2015

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

This article proposes a new information-based subdata selection (IBOSS) algorithm, Squared Scaled Distance Algorithm (SSDA). It is based on the invariance of the determinant of the information matrix under orthogonal transformations, especially rotations. Extensive simulation results show that the new IBOSS algorithm retains nice asymptotic properties of IBOSS and gives a larger determinant of the subdata information matrix. It has the same order of time complexity as the D-optimal IBOSS algorithm. However, it exploits the advantages of vectorized calculation avoiding for loops and is approximately 6 times as fast as the D-optimal IBOSS algorithm in R. The robustness of SSDA …

- Contributors
- Zheng, Yi, Stufken, John, Reiser, Mark, et al.
- Created Date
- 2017

It is common in the analysis of data to provide a goodness-of-fit test to assess the performance of a model. In the analysis of contingency tables, goodness-of-fit statistics are frequently employed when modeling social science, educational or psychological data where the interest is often directed at investigating the association among multi-categorical variables. Pearson's chi-squared statistic is well-known in goodness-of-fit testing, but it is sometimes considered to produce an omnibus test as it gives little guidance to the source of poor fit once the null hypothesis is rejected. However, its components can provide powerful directional tests. In this dissertation, orthogonal components …

- Contributors
- Milovanovic, Jelena, Young, Dennis, Reiser, Mark, et al.
- Created Date
- 2011

Whilst linear mixed models offer a flexible approach to handle data with multiple sources of random variability, the related hypothesis testing for the fixed effects often encounters obstacles when the sample size is small and the underlying distribution for the test statistic is unknown. Consequently, five methods of denominator degrees of freedom approximations (residual, containment, between-within, Satterthwaite, Kenward-Roger) are developed to overcome this problem. This study aims to evaluate the performance of these five methods with a mixed model consisting of random intercept and random slope. Specifically, simulations are conducted to provide insights on the F-statistics, denominator degrees of freedom …

- Contributors
- Huang, Ping-Chieh, Reiser, Mark, Kao, Ming-Hung, et al.
- Created Date
- 2020

When analyzing longitudinal data it is essential to account both for the correlation inherent from the repeated measures of the responses as well as the correlation realized on account of the feedback created between the responses at a particular time and the predictors at other times. A generalized method of moments (GMM) for estimating the coefficients in longitudinal data is presented. The appropriate and valid estimating equations associated with the time-dependent covariates are identified, thus providing substantial gains in efficiency over generalized estimating equations (GEE) with the independent working correlation. Identifying the estimating equations for computation is of utmost importance. …

- Contributors
- Yin, Jianqiong, Wilson, Jeffrey Wilson, Reiser, Mark, et al.
- Created Date
- 2012

In the presence of correlation, generalized linear models cannot be employed to obtain regression parameter estimates. To appropriately address the extravariation due to correlation, methods to estimate and model the additional variation are investigated. A general form of the mean-variance relationship is proposed which incorporates the canonical parameter. The two variance parameters are estimated using generalized method of moments, negating the need for a distributional assumption. The mean-variance relation estimates are applied to clustered data and implemented in an adjusted generalized quasi-likelihood approach through an adjustment to the covariance matrix. In the presence of significant correlation in hierarchical structured data, …

- Contributors
- Irimata, Katherine, Wilson, Jeffrey R, Kamarianakis, Ioannis, et al.
- Created Date
- 2018