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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.


Subject
Date Range
2010 2019


The comparison of between- versus within-person relations addresses a central issue in psychological research regarding whether group-level relations among variables generalize to individual group members. Between- and within-person effects may differ in magnitude as well as direction, and contextual multilevel models can accommodate this difference. Contextual multilevel models have been explicated mostly for cross-sectional data, but they can also be applied to longitudinal data where level-1 effects represent within-person relations and level-2 effects represent between-person relations. With longitudinal data, estimating the contextual effect allows direct evaluation of whether between-person and within-person effects differ. Furthermore, these models, unlike single-level models, permit …

Contributors
Wurpts, Ingrid Carlson, MacKinnon, David P, West, Stephen G, et al.
Created Date
2016

Through a two study simulation design with different design conditions (sample size at level 1 (L1) was set to 3, level 2 (L2) sample size ranged from 10 to 75, level 3 (L3) sample size ranged from 30 to 150, intraclass correlation (ICC) ranging from 0.10 to 0.50, model complexity ranging from one predictor to three predictors), this study intends to provide general guidelines about adequate sample sizes at three levels under varying ICC conditions for a viable three level HLM analysis (e.g., reasonably unbiased and accurate parameter estimates). In this study, the data generating parameters for the were obtained …

Contributors
Yel, Nedim, Levy, Roy, Elliott, Stephen N, et al.
Created Date
2016

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 …

Contributors
Burke, Sarah Ellen, Montgomery, Douglas C, Borror, Connie M, et al.
Created Date
2016

As the world embraces a sustainable energy future, alternative energy resources, such as wind power, are increasingly being seen as an integral part of the future electric energy grid. Ultimately, integrating such a dynamic and variable mix of generation requires a better understanding of renewable generation output, in addition to power grid systems that improve power system operational performance in the presence of anticipated events such as wind power ramps. Because of the stochastic, uncontrollable nature of renewable resources, a thorough and accurate characterization of wind activity is necessary to maintain grid stability and reliability. Wind power ramps from an …

Contributors
Ganger, David Wu, Vittal, Vijay, Zhang, Junshan, et al.
Created Date
2016

The operating temperature of photovoltaic (PV) modules is affected by external factors such as irradiance, wind speed and ambient temperature as well as internal factors like material properties and design properties. These factors can make a difference in the operating temperatures between cells within a module and between modules within a plant. This is a three-part thesis. Part 1 investigates the behavior of temperature distribution of PV cells within a module through outdoor temperature monitoring under various operating conditions (Pmax, Voc and Isc) and examines deviation in the temperature coefficient values pertaining to this temperature variation. ANOVA, a statistical tool, …

Contributors
PAVGI, ASHWINI, Tamizhmani, Govindasamy, Phelan, Patrick, et al.
Created Date
2016

Urban growth, from regional sprawl to global urbanization, is the most rapid, drastic, and irreversible form of human modification to the natural environment. Extensive land cover modifications during urban growth have altered the local energy balance, causing the city warmer than its surrounding rural environment, a phenomenon known as an urban heat island (UHI). How are the seasonal and diurnal surface temperatures related to the land surface characteristics, and what land cover types and/or patterns are desirable for ameliorating climate in a fast growing desert city? This dissertation scrutinizes these questions and seeks to address them using a combination of …

Contributors
Fan, Chao, Myint, Soe W, Li, Wenwen, et al.
Created Date
2016

The Partition of Variance (POV) method is a simplistic way to identify large sources of variation in manufacturing systems. This method identifies the variance by estimating the variance of the means (between variance) and the means of the variance (within variance). The project shows that the method correctly identifies the variance source when compared to the ANOVA method. Although the variance estimators deteriorate when varying degrees of non-normality is introduced through simulation; however, the POV method is shown to be a more stable measure of variance in the aggregate. The POV method also provides non-negative, stable estimates for interaction when …

Contributors
Little, David John, Borror, Connie, Montgomery, Douglas, et al.
Created Date
2015

Given the importance of buildings as major consumers of resources worldwide, several organizations are working avidly to ensure the negative impacts of buildings are minimized. The U.S. Green Building Council's (USGBC) Leadership in Energy and Environmental Design (LEED) rating system is one such effort to recognize buildings that are designed to achieve a superior performance in several areas including energy consumption and indoor environmental quality (IEQ). The primary objectives of this study are to investigate the performance of LEED certified facilities in terms of energy consumption and occupant satisfaction with IEQ, and introduce a framework to assess the performance of …

Contributors
Chokor, Abbas, El Asmar, Mounir, Chong, Oswald, et al.
Created Date
2015

Functional or dynamic responses are prevalent in experiments in the fields of engineering, medicine, and the sciences, but proposals for optimal designs are still sparse for this type of response. Experiments with dynamic responses result in multiple responses taken over a spectrum variable, so the design matrix for a dynamic response have more complicated structures. In the literature, the optimal design problem for some functional responses has been solved using genetic algorithm (GA) and approximate design methods. The goal of this dissertation is to develop fast computer algorithms for calculating exact D-optimal designs. First, we demonstrated how the traditional exchange …

Contributors
Saleh, Moein, Pan, Rong, Montgomery, Douglas C, et al.
Created Date
2015

Researchers are often interested in estimating interactions in multilevel models, but many researchers assume that the same procedures and interpretations for interactions in single-level models apply to multilevel models. However, estimating interactions in multilevel models is much more complex than in single-level models. Because uncentered (RAS) or grand mean centered (CGM) level-1 predictors in two-level models contain two sources of variability (i.e., within-cluster variability and between-cluster variability), interactions involving RAS or CGM level-1 predictors also contain more than one source of variability. In this Master’s thesis, I use simulations to demonstrate that ignoring the four sources of variability in a …

Contributors
Mazza, Gina Lynn, Enders, Craig K., Aiken, Leona S., et al.
Created Date
2015

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 …

Contributors
Hassler, Edgar, Montgomery, Douglas C, Silvestrini, Rachel T, et al.
Created Date
2015

Although models for describing longitudinal data have become increasingly sophisticated, the criticism of even foundational growth curve models remains challenging. The challenge arises from the need to disentangle data-model misfit at multiple and interrelated levels of analysis. Using posterior predictive model checking (PPMC)—a popular Bayesian framework for model criticism—the performance of several discrepancy functions was investigated in a Monte Carlo simulation study. The discrepancy functions of interest included two types of conditional concordance correlation (CCC) functions, two types of R2 functions, two types of standardized generalized dimensionality discrepancy (SGDDM) functions, the likelihood ratio (LR), and the likelihood ratio difference test …

Contributors
Fay, Derek M., Levy, Roy, Thompson, Marilyn, et al.
Created Date
2015

Currently, there is a clear gap in the missing data literature for three-level models. To date, the literature has only focused on the theoretical and algorithmic work required to implement three-level imputation using the joint model (JM) method of imputation, leaving relatively no work done on fully conditional specication (FCS) method. Moreover, the literature lacks any methodological evaluation of three-level imputation. Thus, this thesis serves two purposes: (1) to develop an algorithm in order to implement FCS in the context of a three-level model and (2) to evaluate both imputation methods. The simulation investigated a random intercept model under both …

Contributors
Keller, Brian Tinnell, Enders, Craig K, Grimm, Kevin J, et al.
Created Date
2015

This is a two-part thesis: Part 1 characterizes soiling losses using various techniques to understand the effect of soiling on photovoltaic modules. The higher the angle of incidence (AOI), the lower will be the photovoltaic (PV) module performance. Our research group has already reported the AOI investigation for cleaned modules of five different technologies with air/glass interface. However, the modules that are installed in the field would invariably develop a soil layer with varying thickness depending on the site condition, rainfall and tilt angle. The soiled module will have the air/soil/glass interface rather than air/glass interface. This study investigates the …

Contributors
Boppana, Sravanthi, Tamizhmani, Govindasamy, Srinivasan, Devarajan, et al.
Created Date
2015

Tracking targets in the presence of clutter is inevitable, and presents many challenges. Additionally, rapid, drastic changes in clutter density between different environments or scenarios can make it even more difficult for tracking algorithms to adapt. A novel approach to target tracking in such dynamic clutter environments is proposed using a particle filter (PF) integrated with Interacting Multiple Models (IMMs) to compensate and adapt to the transition between different clutter densities. This model was implemented for the case of a monostatic sensor tracking a single target moving with constant velocity along a two-dimensional trajectory, which crossed between regions of drastically …

Contributors
Dutson, Karl J, Papandreou-Suppappola, Antonia, Kovvali, Narayan, et al.
Created Date
2015

Complex systems are pervasive in science and engineering. Some examples include complex engineered networks such as the internet, the power grid, and transportation networks. The complexity of such systems arises not just from their size, but also from their structure, operation (including control and management), evolution over time, and that people are involved in their design and operation. Our understanding of such systems is limited because their behaviour cannot be characterized using traditional techniques of modelling and analysis. As a step in model development, statistically designed screening experiments may be used to identify the main effects and interactions most significant …

Contributors
Aldaco-Gastelum, Abraham Netzahualcoyotl, Syrotiuk, Violet R., Colbourn, Charles J., et al.
Created Date
2015

Methods to test hypotheses of mediated effects in the pretest-posttest control group design are understudied in the behavioral sciences (MacKinnon, 2008). Because many studies aim to answer questions about mediating processes in the pretest-posttest control group design, there is a need to determine which model is most appropriate to test hypotheses about mediating processes and what happens to estimates of the mediated effect when model assumptions are violated in this design. The goal of this project was to outline estimator characteristics of four longitudinal mediation models and the cross-sectional mediation model. Models were compared on type 1 error rates, statistical …

Contributors
Valente, Matthew, MacKinnon, David, West, Stephen, et al.
Created Date
2015

Missing data are common in psychology research and can lead to bias and reduced power if not properly handled. Multiple imputation is a state-of-the-art missing data method recommended by methodologists. Multiple imputation methods can generally be divided into two broad categories: joint model (JM) imputation and fully conditional specification (FCS) imputation. JM draws missing values simultaneously for all incomplete variables using a multivariate distribution (e.g., multivariate normal). FCS, on the other hand, imputes variables one at a time, drawing missing values from a series of univariate distributions. In the single-level context, these two approaches have been shown to be equivalent …

Contributors
Mistler, Stephen Andrew, Enders, Craig K, Aiken, Leona, et al.
Created Date
2015

Many methodological approaches have been utilized to predict student retention and persistence over the years, yet few have utilized a Bayesian framework. It is believed this is due in part to the absence of an established process for guiding educational researchers reared in a frequentist perspective into the realms of Bayesian analysis and educational data mining. The current study aimed to address this by providing a model-building process for developing a Bayesian network (BN) that leveraged educational data mining, Bayesian analysis, and traditional iterative model-building techniques in order to predict whether community college students will stop out at the completion …

Contributors
Arcuria, Phil, Levy, Roy, Green, Samuel B, et al.
Created Date
2015

The present thesis explores how statistical methods are conceptualized, used, and interpreted in quantitative Hispanic sociolinguistics in light of the group of statistical methods espoused by Kline (2013) and named by Cumming (2012) as the “new statistics.” The new statistics, as a conceptual framework, repudiates null hypothesis statistical testing (NHST) and replaces it with the ESCI method, or Effect Sizes and Confidence Intervals, as well as meta-analytic thinking. In this thesis, a descriptive review of 44 studies found in three academic journals over the last decade (2005 – 2015), NHST was found to have a tight grip on most researchers. …

Contributors
Kidhardt, Paul Adrian, Cerron-Palomino, Alvaro, Gonzalez-Lopez, Veronica, et al.
Created Date
2015

Threshold regression is used to model regime switching dynamics where the effects of the explanatory variables in predicting the response variable depend on whether a certain threshold has been crossed. When regime-switching dynamics are present, new estimation problems arise related to estimating the value of the threshold. Conventional methods utilize an iterative search procedure, seeking to minimize the sum of squares criterion. However, when unnecessary variables are included in the model or certain variables drop out of the model depending on the regime, this method may have high variability. This paper proposes Lasso-type methods as an alternative to ordinary least …

Contributors
van Schaijik, Maria, Kamarianakis, Yiannis, Kamarianakis, Yiannis, et al.
Created Date
2015

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

Smoking remains the leading cause of preventable death in the United States, and early initiation is associated with greater difficulty quitting. Among adolescent smokers, those with attention-deficit hyperactivity disorder (ADHD), characterized by difficulties associated with impulsivity, hyperactivity, and inattention, smoke at nearly twice the rate of their peers. Although cigarette smoking is highly addictive, nicotine is a relatively weak primary reinforcer, spurring research on other potential targets that may maintain smoking, including the potential benefits of nicotine on attention, inhibition, and reinforcer efficacy. The present study employs the most prevalent rodent model of ADHD, the spontaneously hypertensive rat (SHR) and …

Contributors
Mazur, Gabriel Joseph, Sanabria, Federico, Killeen, Peter R, et al.
Created Date
2014

In this era of fast computational machines and new optimization algorithms, there have been great advances in Experimental Designs. We focus our research on design issues in generalized linear models (GLMs) and functional magnetic resonance imaging(fMRI). The first part of our research is on tackling the challenging problem of constructing exact designs for GLMs, that are robust against parameter, link and model uncertainties by improving an existing algorithm and providing a new one, based on using a continuous particle swarm optimization (PSO) and spectral clustering. The proposed algorithm is sufficiently versatile to accomodate most popular design selection criteria, and we …

Contributors
Temkit, M'Hamed, Kao, Jason, Reiser, Mark, et al.
Created Date
2014

Urban scaling analysis has introduced a new scientific paradigm to the study of cities. With it, the notions of <italic>size</italic>, <italic>heterogeneity</italic> and <italic>structure</italic> have taken a leading role. These notions are assumed to be behind the causes for why cities differ from one another, sometimes wildly. However, the mechanisms by which size, heterogeneity and structure shape the general statistical patterns that describe urban economic output are still unclear. Given the rapid rate of urbanization around the globe, we need precise and formal mathematical understandings of these matters. In this context, I perform in this dissertation probabilistic, distributional and computational explorations …

Contributors
Gomez-Lievano, Andres, Lobo, José, Muneepeerakul, Rachata, et al.
Created Date
2014

The objective of this thesis is to investigate the various types of energy end-uses to be expected in future high efficiency single family residences. For this purpose, this study has analyzed monitored data from 14 houses in the 2013 Solar Decathlon competition, and segregates the energy consumption patterns in various residential end-uses (such as lights, refrigerators, washing machines, ...). The analysis was not straight-forward since these homes were operated according to schedules previously determined by the contest rules. The analysis approach allowed the isolation of the comfort energy use by the Heating, Venting and Cooling (HVAC) systems. HVAC are the …

Contributors
Garkhail, Rahul, Reddy, T Agami, Bryan, Harvey, et al.
Created Date
2014

In the field of infectious disease epidemiology, the assessment of model robustness outcomes plays a significant role in the identification, reformulation, and evaluation of preparedness strategies aimed at limiting the impact of catastrophic events (pandemics or the deliberate release of biological agents) or used in the management of disease prevention strategies, or employed in the identification and evaluation of control or mitigation measures. The research work in this dissertation focuses on: The comparison and assessment of the role of exponentially distributed waiting times versus the use of generalized non-exponential parametric distributed waiting times of infectious periods on the quantitative and …

Contributors
Morale Butler, Emmanuel Jesús, Castillo-Chavez, Carlos, Aparicio, Juan P, et al.
Created Date
2014

Extraordinary medical advances have led to significant reductions in the burden of infectious diseases in humans. However, infectious diseases still account for more than 13 million annual deaths. This large burden is partly due to some pathogens having found suitable conditions to emerge and spread in denser and more connected host populations, and others having evolved to escape the pressures imposed by the rampant use of antimicrobials. It is then critical to improve our understanding of how diseases spread in these modern landscapes, characterized by new host population structures and socio-economic environments, as well as containment measures such as the …

Contributors
Patterson-Lomba, Oscar, Castillo-Chavez, Carlos, Towers, Sherry, et al.
Created Date
2014

This simulation study compared the utility of various discrepancy measures within a posterior predictive model checking (PPMC) framework for detecting different types of data-model misfit in multidimensional Bayesian network (BN) models. The investigated conditions were motivated by an applied research program utilizing an operational complex performance assessment within a digital-simulation educational context grounded in theories of cognition and learning. BN models were manipulated along two factors: latent variable dependency structure and number of latent classes. Distributions of posterior predicted p-values (PPP-values) served as the primary outcome measure and were summarized in graphical presentations, by median values across replications, and by …

Contributors
Crawford, Aaron Vaughn, Levy, Roy, Green, Samuel, et al.
Created Date
2014

The main objective of this research is to develop an approach to PV module lifetime prediction. In doing so, the aim is to move from empirical generalizations to a formal predictive science based on data-driven case studies of the crystalline silicon PV systems. The evaluation of PV systems aged 5 to 30 years old that results in systematic predictive capability that is absent today. The warranty period provided by the manufacturers typically range from 20 to 25 years for crystalline silicon modules. The end of lifetime (for example, the time-to-degrade by 20% from rated power) of PV modules is usually …

Contributors
Kuitche, Joseph Mathurin, Pan, Rong, TamizhMani, Govindasamy, et al.
Created Date
2014

This thesis presents a meta-analysis of lead-free solder reliability. The qualitative analyses of the failure modes of lead- free solder under different stress tests including drop test, bend test, thermal test and vibration test are discussed. The main cause of failure of lead- free solder is fatigue crack, and the speed of propagation of the initial crack could differ from different test conditions and different solder materials. A quantitative analysis about the fatigue behavior of SAC lead-free solder under thermal preconditioning process is conducted. This thesis presents a method of making prediction of failure life of solder alloy by building …

Contributors
Xu, Xinyue, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2014

This is a two part thesis: Part 1 of this thesis determines the most dominant failure modes of field aged photovoltaic (PV) modules using experimental data and statistical analysis, FMECA (Failure Mode, Effect, and Criticality Analysis). The failure and degradation modes of about 5900 crystalline-Si glass/polymer modules fielded for 6 to 16 years in three different photovoltaic (PV) power plants with different mounting systems under the hot-dry desert climate of Arizona are evaluated. A statistical reliability tool, FMECA that uses Risk Priority Number (RPN) is performed for each PV power plant to determine the dominant failure modes in the modules …

Contributors
Shrestha, Sanjay Mohan, Tamizhmani, Govindsamy, Srinivasan, Devrajan, et al.
Created Date
2014

Technological advances have enabled the generation and collection of various data from complex systems, thus, creating ample opportunity to integrate knowledge in many decision making applications. This dissertation introduces holistic learning as the integration of a comprehensive set of relationships that are used towards the learning objective. The holistic view of the problem allows for richer learning from data and, thereby, improves decision making. The first topic of this dissertation is the prediction of several target attributes using a common set of predictor attributes. In a holistic learning approach, the relationships between target attributes are embedded into the learning algorithm …

Contributors
Azarnoush, Bahareh, Runger, George C, Bekki, Jennifer, et al.
Created Date
2014

Obtaining high-quality experimental designs to optimize statistical efficiency and data quality is quite challenging for functional magnetic resonance imaging (fMRI). The primary fMRI design issue is on the selection of the best sequence of stimuli based on a statistically meaningful optimality criterion. Some previous studies have provided some guidance and powerful computational tools for obtaining good fMRI designs. However, these results are mainly for basic experimental settings with simple statistical models. In this work, a type of modern fMRI experiments is considered, in which the design matrix of the statistical model depends not only on the selected design, but also …

Contributors
Zhou, Lin, Kao, Ming-hung, Reiser, Mark, et al.
Created Date
2014

Statistics is taught at every level of education, yet teachers often have to assume their students have no knowledge of statistics and start from scratch each time they set out to teach statistics. The motivation for this experimental study comes from interest in exploring educational applications of augmented reality (AR) delivered via mobile technology that could potentially provide rich, contextualized learning for understanding concepts related to statistics education. This study examined the effects of AR experiences for learning basic statistical concepts. Using a 3 x 2 research design, this study compared learning gains of 252 undergraduate and graduate students from …

Contributors
Conley, Quincy, Atkinson, Robert K, Nguyen, Frank, et al.
Created Date
2013

Nowadays product reliability becomes the top concern of the manufacturers and customers always prefer the products with good performances under long period. In order to estimate the lifetime of the product, accelerated life testing (ALT) is introduced because most of the products can last years even decades. Much research has been done in the ALT area and optimal design for ALT is a major topic. This dissertation consists of three main studies. First, a methodology of finding optimal design for ALT with right censoring and interval censoring have been developed and it employs the proportional hazard (PH) model and generalized …

Contributors
Yang, Tao, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2013

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

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&rsquo; test scores as outcome variables and teachers&rsquo; 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

Dimensionality assessment is an important component of evaluating item response data. Existing approaches to evaluating common assumptions of unidimensionality, such as DIMTEST (Nandakumar & Stout, 1993; Stout, 1987; Stout, Froelich, & Gao, 2001), have been shown to work well under large-scale assessment conditions (e.g., large sample sizes and item pools; see e.g., Froelich & Habing, 2007). It remains to be seen how such procedures perform in the context of small-scale assessments characterized by relatively small sample sizes and/or short tests. The fact that some procedures come with minimum allowable values for characteristics of the data, such as the number of …

Contributors
Reichenberg, Ray E., Levy, Roy, Thompson, Marilyn S., et al.
Created Date
2013

Parallel Monte Carlo applications require the pseudorandom numbers used on each processor to be independent in a probabilistic sense. The TestU01 software package is the standard testing suite for detecting stream dependence and other properties that make certain pseudorandom generators ineffective in parallel (as well as serial) settings. TestU01 employs two basic schemes for testing parallel generated streams. The first applies serial tests to the individual streams and then tests the resulting P-values for uniformity. The second turns all the parallel generated streams into one long vector and then applies serial tests to the resulting concatenated stream. Various forms of …

Contributors
Ismay, Chester Ivan, Eubank, Randall, Young, Dennis, et al.
Created Date
2013

Statistical process control (SPC) and predictive analytics have been used in industrial manufacturing and design, but up until now have not been applied to threshold data of vital sign monitoring in remote care settings. In this study of 20 elders with COPD and/or CHF, extended months of peak flow monitoring (FEV1) using telemedicine are examined to determine when an earlier or later clinical intervention may have been advised. This study demonstrated that SPC may bring less than a 2.0% increase in clinician workload while providing more robust statistically-derived thresholds than clinician-derived thresholds. Using a random K-fold model, FEV1 output was …

Contributors
Fralick, Celeste Rachelle, Muthuswamy, Jitendran, O'Shea, Terrance, et al.
Created Date
2013

The use of bias indicators in psychological measurement has been contentious, with some researchers questioning whether they actually suppress or moderate the ability of substantive psychological indictors to discriminate (McGrath, Mitchell, Kim, & Hough, 2010). Bias indicators on the MMPI-2-RF (F-r, Fs, FBS-r, K-r, and L-r) were tested for suppression or moderation of the ability of the RC1 and NUC scales to discriminate between Epileptic Seizures (ES) and Non-epileptic Seizures (NES, a conversion disorder that is often misdiagnosed as ES). RC1 and NUC had previously been found to be the best scales on the MMPI-2-RF to differentiate between ES and …

Contributors
Wershba, Rebecca Eve, Lanyon, Richard I, Barrera, Manuel, et al.
Created Date
2013

With the increase in computing power and availability of data, there has never been a greater need to understand data and make decisions from it. Traditional statistical techniques may not be adequate to handle the size of today's data or the complexities of the information hidden within the data. Thus knowledge discovery by machine learning techniques is necessary if we want to better understand information from data. In this dissertation, we explore the topics of asymmetric loss and asymmetric data in machine learning and propose new algorithms as solutions to some of the problems in these topics. We also studied …

Contributors
Koh, Derek, Runger, George, Wu, Tong, et al.
Created Date
2013

Estimating cointegrating relationships requires specific techniques. Canonical correlations are used to determine the rank and space of the cointegrating matrix. The vectors used to transform the data into canonical variables have an eigenvector representation, and the associated canonical correlations have an eigenvalue representation. The number of cointegrating relations is chosen based upon a theoretical difference in the convergence rates of the eignevalues. The number of cointegrating relations is consistently estimated using a threshold function which places a lower bound on the eigenvalues associated with cointegrating relations and an upper bound on the eigenvalues on the eigenvalues not associated with cointegrating …

Contributors
Nowak, Adam Daniel, Ahn, Seung C, Liu, Crocker, et al.
Created Date
2012

Temporal data are increasingly prevalent and important in analytics. Time series (TS) data are chronological sequences of observations and an important class of temporal data. Fields such as medicine, finance, learning science and multimedia naturally generate TS data. Each series provide a high-dimensional data vector that challenges the learning of the relevant patterns This dissertation proposes TS representations and methods for supervised TS analysis. The approaches combine new representations that handle translations and dilations of patterns with bag-of-features strategies and tree-based ensemble learning. This provides flexibility in handling time-warped patterns in a computationally efficient way. The ensemble learners provide a …

Contributors
Baydogan, Mustafa Gokce, Runger, George C, Atkinson, Robert, et al.
Created Date
2012

This dissertation involves three problems that are all related by the use of the singular value decomposition (SVD) or generalized singular value decomposition (GSVD). The specific problems are (i) derivation of a generalized singular value expansion (GSVE), (ii) analysis of the properties of the chi-squared method for regularization parameter selection in the case of nonnormal data and (iii) formulation of a partial canonical correlation concept for continuous time stochastic processes. The finite dimensional SVD has an infinite dimensional generalization to compact operators. However, the form of the finite dimensional GSVD developed in, e.g., Van Loan does not extend directly to …

Contributors
Huang, Qing, Eubank, Randall, Renaut, Rosemary, et al.
Created Date
2012

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

A least total area of triangle method was proposed by Teissier (1948) for fitting a straight line to data from a pair of variables without treating either variable as the dependent variable while allowing each of the variables to have measurement errors. This method is commonly called Reduced Major Axis (RMA) regression and is often used instead of Ordinary Least Squares (OLS) regression. Results for confidence intervals, hypothesis testing and asymptotic distributions of coefficient estimates in the bivariate case are reviewed. A generalization of RMA to more than two variables for fitting a plane to data is obtained by minimizing …

Contributors
Li, Jingjin, Young, Dennis, Eubank, Randall, et al.
Created Date
2012

This thesis examines the application of statistical signal processing approaches to data arising from surveys intended to measure psychological and sociological phenomena underpinning human social dynamics. The use of signal processing methods for analysis of signals arising from measurement of social, biological, and other non-traditional phenomena has been an important and growing area of signal processing research over the past decade. Here, we explore the application of statistical modeling and signal processing concepts to data obtained from the Global Group Relations Project, specifically to understand and quantify the effects and interactions of social psychological factors related to intergroup conflicts. We …

Contributors
Liu, Hui, Taylor, Thomas, Cochran, Douglas, et al.
Created Date
2012

Photovoltaic (PV) modules are typically rated at three test conditions: STC (standard test conditions), NOCT (nominal operating cell temperature) and Low E (low irradiance). The current thesis deals with the power rating of PV modules at twenty-three test conditions as per the recent International Electrotechnical Commission (IEC) standard of IEC 61853 &ndash; 1. In the current research, an automation software tool developed by a previous researcher of ASU &ndash; PRL (ASU Photovoltaic Reliability Laboratory) is validated at various stages. Also in the current research, the power rating of PV modules for four different manufacturers is carried out according to IEC …

Contributors
Vemula, Meena Gupta, Tamizhmani, Govindasamy, Macia, Narcio F., et al.
Created Date
2012