Statistics and Probability Commons^™

Hiding In Plain Sight: Accounting For Rate Heterogeneity In Trait Evolution Models, James Boyko

Graduate Theses and Dissertations

Within the last four decades, phylogenetic comparative methods have become the defacto method of analysis for comparative biologists. The availability of high-quality comparative datasets has been matched by an explosion of possible phylogenetic models. In large part, the efforts to increase the realism of phylogenetic comparative methods has been successful as evidenced by their widespread use. To this extensive literature, my contributions are modest. I have focused my dissertation work on two main themes. First, most phenotypic evolution is not independent of other phenotypes. Changes in a particular character may influence changes in another and modeling these characters in isolation …

Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell

All Dissertations

Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …

Quantile Differences In The Age-Related Decline In Cardiorespiratory Fitness Between Sexes In Adults Without Type 2 Diabetes Mellitus In The United States, Andrew Ortaglia, Melissa Stansbury, Michael David Wirth, Xuemei Sui, Matteo Bottai

Faculty Publications

Objective: To comprehensively assess the extent to which the decline in cardiorespiratory fitness (CRF) with age differs between sexes. Participants and Methods: This study used data from the Aerobics Center Longitudinal Study, conducted between September 1974 and August 2006, consisting primarily of White adults from middle-to-upper socioeconomic strata restricted to adults without type 2 diabetes mellitus (33,742 men and 9,415 women). Quantile regression models were used to estimate the differences in age-associated changes in CRF between the sexes, estimated using a maximal treadmill test. Results: For adults aged up to 45 years, significant differences in slopes relating to age and …

Defining Areas Of Interest Using Voronoi And Modified Voronoi Tesselations To Analyze Eye-Tracking Data, Joanna D. Coltrin

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Eye tracking is a technology used to track where someone is looking. Eye-tracking technology is often used to study what people focus on when looking at a photo of another person. The eye-tracking technology records points on a photo that a person is looking at. When the photo being looked at shows a person, the points can be categorized by body part such as head, right hand, left hand, and torso. This thesis presents the use of partially circular areas to define the body parts of the person in the photo and therefore categorize the points collected by the eye-tracker. …

Redefining Nba Basketball Positions Through Visualization And Mega-Cluster Analysis, Alexander L. Hedquist

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Basketball players have historically been classified based on one of five positions, namely Point Guards, Shooting Guards, Small Forwards, and Centers. While grouping players into these five categories may provide general descriptions of their perceived role, these standard positions fall short of describing players based on their true abilities and performance. This MS thesis proposes a method to group players of the National Basketball Association (NBA) from the past 20 seasons into more meaningful and specific player positions. We systematically group these players into nine distinct categories, and we draw from a vast array of visualization tools, techniques, and software …

Contributions To Random Forest Variable Importance With Applications In R, Kelvyn K. Bladen

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

A major focus in statistics is building and improving computational algorithms that can use data to predict a response. Two fundamental camps of research arise from such a goal. The first camp is researching ways to get more accurate predictions. Many sophisticated methods, collectively known as machine learning methods, have been developed for this very purpose. One such method that is widely used across industry and many other areas of investigation is called Random Forests.

The second camp of research is that of improving the interpretability of machine learning methods. This is worthy of attention when analysts desire to optimize …

Geometry- And Accuracy-Preserving Random Forest Proximities With Applications, Jake S. Rhodes

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Many machine learning algorithms use calculated distances or similarities between data observations to make predictions, cluster similar data, visualize patterns, or generally explore the data. Most distances or similarity measures do not incorporate known data labels and are thus considered unsupervised. Supervised methods for measuring distance exist which incorporate data labels and thereby exaggerate separation between data points of different classes. This approach tends to distort the natural structure of the data. Instead of following similar approaches, we leverage a popular algorithm used for making data-driven predictions, known as random forests, to naturally incorporate data labels into similarity measures known …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Semiparametric Estimation With Clustered Right Censored Data Via Multivariate Gaussian Random Fields, Fathima Zahra Sainul Abdeen

Doctoral Dissertations

Consider a fixed number of clustered areas identified by their geographical coordinate that are monitored for the occurrences of an event such as pandemic, epidemic, migration to name a few. Data collected on units at all areas include time varying covariates and other environmental factors that may affect event occurrences. The event times in every area can be independent. They can also be correlated with correlation between two units induced by an unobservable frailty. In both cases, the collected data is considered pairwise to account for spatial correlation between all pair of areas. The pairwise right censored data is probit-transformed …

Improving Computation For Hierarchical Bayesian Spatial Gaussian Mixture Models With Application To The Analysis Of Thz Image Of Breast Tumor, Jean Remy Habimana

Graduate Theses and Dissertations

In the first chapter of this dissertation we give a brief introduction to Markov chain Monte Carlo methods (MCMC) and their application in Bayesian inference. In particular, we discuss the Metropolis-Hastings and conjugate Gibbs algorithms and explore the computational underpinnings of these methods. The second chapter discusses how to incorporate spatial autocorrelation in linear a regression model with an emphasis on the computational framework for estimating the spatial correlation patterns.

The third chapter starts with an overview of Gaussian mixture models (GMMs). However, because in the GMM framework the observations are assumed to be independent, GMMs are less effective when …

Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura

Mathematics & Statistics ETDs

This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of …

Robust Uncertainty Quantification With Analysis Of Error In Standard And Non-Standard Quantities Of Interest, Zachary Stevens

Mathematics & Statistics ETDs

This thesis derives two Uncertainty Quantification (UQ) methods for differential equations that depend on random parameters: (\textbf{i}) error bounds for a computed cumulative distribution function (\textbf{ii}) a multi-level Monte Carlo (MLMC) algorithm with adaptively refined meshes and accurately computed stopping-criteria. Both UQ approaches utilize adjoint-based \textit{a posteriori} error analysis in order to accurately estimate the error in samples of numerically approximated quantities of interest. The adaptive MLMC algorithm developed in this thesis relies on the adjoint-based error analysis to adaptively create meshes and accurately monitor a stopping criteria. This is in contrast to classical MLMC algorithms which employ either a …

Changes Overtime In Perinatal Management And Outcomes Of Extremely Preterm Infants In One Tertiary Care Romanian Center, Diana Ungureanu, Nansi Boghossian, Laura Mihaela Suciu

Faculty Publications

Background and Objectives: Extremely preterm infants were at increased risk of mortality and morbidity. The purpose of this study was to: (1) examine changes over time in perinatal management, mortality, and major neonatal morbidities among infants born at 25⁰–28⁶ weeks’ gestational age and cared for at one Romanian tertiary care unit and (2) compare the differences with available international data. Material and Methods: This study consisted of infants born at 25⁰–28⁶ weeks in one tertiary neonatal academic center in Romania during two 4-year periods (2007–2010 and 2015–2018). Major morbidities were defined as any of …

Random Walks In The Quarter Plane: Solvable Models With An Analytical Approach, Harshita Bali, Enrico Au-Yeung

DePaul Discoveries

Initially, an urn contains 3 blue balls and 1 red ball. A ball is randomly chosen from the urn. The ball is returned to the urn, together with one additional ball of the same type (red or blue). When the urn has twenty balls in it, what is the probability that exactly ten balls are blue? This is a model for a random process. This urn model has been extended in various ways and we consider some of these generalizations. Urn models can be formulated as random walks in the quarter plane. Our findings indicate that for a specific type …

Spotted Fever Group Rickettsioses In Central America: The Research And Public Health Disparity Among Socioeconomic Lines, Kyndall C. Dye-Braumuller, Marvin S. Rodriguez Aquino, Stella C.W. Self, Mufaro Kanyangarara, Melissa Nolan Ph.D., Mph

Faculty Publications

Tick-borne diseases including rickettsial diseases are increasing in incidence worldwide. Many rickettsial pathogens can cause disease which is commonly underdiagnosed and underreported; Rickettsia pathogens in the spotted fever group (SFGR) are thus classified as neglected bacterial pathogens. The Central American region shoulders a large proportion of the global neglected disease burden; however, little is known regarding SFGR disease here. Although development varies, four of the seven countries in this region have both the highest poverty rates and SFGR disease burdens (El Salvador, Honduras, Guatemala, and Nicaragua), compared to Belize, Panama, and Costa Rica. Utilizing the Human Development Index (HDI), we …

Joint Models For Repeated Measured, Non-Normally Distributed Multilevel Data, Weiwei He

USF Tampa Graduate Theses and Dissertations

Clinical trials have tended to collect both survival information and longitudinal biomarkers, as well as other covariates. In order to better assess the severity of diverse diseases, we need to collect various longitudinal outcomes. Furthermore, longitudinal data could consist of a number of different measurements of varying types. The multilevel item response theory (MLIRT) model has been widely used in several fields such as public health and health sciences for multivariate longitudinal outcomes. Joint models combining the longitudinal and survival processes, as well as the relation between them, have been developed to minimize bias and improve the efficiency of estimates. …

Capturing The Pool Dilution Effect In Group Testing Regression: A Bayesian Approach, Stella Self Ph.D., Ms, Christopher Mcmahan, Stefani Mokalled

Faculty Publications

Group (pooled) testing is becoming a popular strategy for screening large populations for infectious diseases. This popularity is owed to the cost savings that can be realized through implementing group testing methods. These methods involve physically combining biomaterial (eg, saliva, blood, urine) collected on individuals into pooled specimens which are tested for an infection of interest. Through testing these pooled specimens, group testing methods reduce the cost of diagnosing all individuals under study by reducing the number of tests performed. Even though group testing offers substantial cost reductions, some practitioners are hesitant to adopt group testing methods due to the …

The Impact Of Service Dogs On Objective And Perceived Sleep Quality For Veterans With Ptsd, Madhuri Vempati, Elise A. Miller, Sarah C. Leighton, Leanne O. Nieforth, Marguerite O’Haire

Discovery Undergraduate Interdisciplinary Research Internship

One in four post-9/11 veterans (Fulton et al., 2015) have been diagnosed with posttraumatic stress disorder (PTSD), facing sleep disruptions as one of their most common symptoms. Service dogs have become an increasingly popular complementary intervention and anecdotes suggest they may impact sleep for veterans with PTSD. There is a need for empirical investigation into these claims through measurement and analysis of sleep quality.

The purpose of this study was to longitudinally investigate the impact of service dogs on sleep quality through both objective and subjective measures.

Participants in the treatment group (n=92) received a service dog after baseline, while …

Concerns With Taking The Covid-19 Vaccine, Kaela Bellamy, Robert S. Keyser

The Kennesaw Journal of Undergraduate Research

This IRB-approved descriptive study provides an overview of the concerns associated with receiving a COVID-19 vaccination within the Kennesaw State University community, an R2 university with over 41,000 students, and uses a survey to provide insight into how students, faculty, staff, and administrators are responding to the vaccinations for COVID-19, both available and unavailable, and their preferences. Our research findings indicate that: 1) Most of the population at Kennesaw State University intends to receive the vaccine, regardless of their concerns; 2) The majority of the participants who are either employed or provided an education by Kennesaw State University plan to …

Spatio-Temporal Models Of Infectious Disease With High Rates Of Asymptomatic Transmission, Aminur Rahman, Angela Peace, Ramesh Kesawan, Souparno Ghosh

Department of Statistics: Faculty Publications

The surprisingly mercurial Covid-19 pandemic has highlighted the need to not only accelerate research on infectious disease, but to also study them using novel techniques and perspectives. A major contributor to the dificulty of containing the current pandemic is due to the highly asymptomatic nature of the disease. In this investigation, we develop a modeling framework to study the spatio-temporal evolution of diseases with high rates of asymptomatic transmission, and we apply this framework to a hypothetical country with mathematically tractable geography; namely, square counties uniformly organized into a rectangle. We first derive a model for the temporal dynamics of …