Digital Commons Network^™

Healthcare Providers’ Perspective On Hiv Testing And Hypothetical Mhealth-Connected Linkage To Care Among Men Who Have Sex With Men (Msm) In South Carolina, Tony Brown, Prince Nii Ossah Addo, Monique J. Brown Ph.D., Mph, Xiaoming Li, Oluwafemi Adeagbo

Faculty Publications

Background: HIV continues to be an important public health concern in South Carolina (SC). However, an examination of providers’ willingness to use mHealth technologies to address ongoing barriers to HIV care and prevention strategies, particularly among men who have sex with men (MSM) is currently lacking in SC. We therefore explored HIV care providers’ perceptions of HIV testing and treatment uptake among MSM, and providers’ willingness to use mHealth technology to address barriers to HIV testing and treatment in SC. Methods: Between August and December 2021, we conducted semistructured virtual interviews with 10 HIV care providers recruited purposively based on …

Sars-Cov-2 Detection And Persistence In A Remote Amazonian Settlement, Glauco M. Silva, Roberto C. Ilacqua, Franciely G. Gonçalves, Carla M. Santana, Felipe T. Jordão, Paula R. Prist, Melissa S. Nolan Ph.D., Mph, Andreia F. Brilhante, Marcia A. Sperança, Gabriel Z. Laporta

Faculty Publications

Background: COVID-19 continues to pose a major global health challenge. Despite its geographic distance from Brazil’s major urban centers, Acre state has experienced notable outbreaks. This study assessed the detection and persistence of SARS-CoV-2 in the rural settlement of Santa Luzia, located in the remote municipality of Cruzeiro do Sul, Acre state, Brazil. Methods: In July 2022, a cross-sectional survey was conducted at 40 sites from an ongoing environmental study, selected by deforestation patterns and proximity to health posts. Saliva samples were collected from residents aged 5–90 years, followed by nucleic acid extraction and multiplex RT-qPCR for SARS-CoV-2 detection. Results: …

A Method For Empirically Assessing Small Area Estimators Via Bootstrap-Weighted K-Nearest-Neighbor Artificial Populations, With Applications To Forest Inventory, Grayson W. White, Jerzy Wieczorek, Zachariah W. Cody, Emily X. Tan, Jacqueline O. Chistolini, Kelly S. Mcconville, Tracey S. Frescino, Gretchen G. Moisen

Faculty Journal Articles

National Forest Inventories monitor forest attributes across a variety of spatial and temporal scales in a given country. Increased interest in reporting and management at smaller scales has driven National Forest Inventories to investigate and adopt small area estimation (SAE) due to the promise of increased precision at these scales. However, comparing and evaluating SAE models for a given application is inherently difficult. Typically, many areas lack enough data to check unit-level modeling assumptions or to assess unit-level predictions empirically; and no ground truth is available for checking area-level estimates. Design-based simulation from artificial populations can help with each of …

Small Area Estimation Of Forest Biomass Via A Two-Stage Model For Continuous Zero-Inflated Data, Grayson W. White, Josh K. Yamamoto, Dinan H. Elsyad, Julian F. Schmitt, Niels H. Korsgaard, Jie Hu, George C. Gaines Iii, Tracey S. Frescino, Kelly S. Mcconville

Faculty Journal Articles

Nationwide Forest Inventories (NFIs) collect data on and monitor the trends of forests across the globe. Users of NFI data are increasingly interested in monitoring forest attributes such as biomass at fine geographic and temporal scales, resulting in a need for assessment and development of small area estimation techniques in forest inventory. We implement a small area estimator and parametric bootstrap estimator that account for zero-inflation in biomass data via a two-stage model-based approach and compare the performance to a Horvitz–Thompson estimator, a post-stratified estimator, and to the unit- and area-level empirical best linear unbiased prediction (EBLUP) estimators. We conduct …

A Two-Stage Design For Choosing Among Several Normal Treatments In Comparison With A Control: The Case Of Common, Known Variance, Samarasuriyage Sayura Sankalpa Senaratne

UNF Graduate Theses and Dissertations

A two-stage design is developed for comparing the means of multiple normally distributed treatment groups under a known common variance, with the aim of identifying the treatment with the highest mean while minimizing the expected sample size, a crucial consideration in clinical trials. The proposed methodology integrates elements of both hypothesis testing and selection procedures to achieve greater efficiency and decision-making power. In the initial stage, if no treatment exhibits a mean surpassing a predefined efficacy threshold, the trial is terminated early, conserving resources. If one or more treatments exceed the threshold, the procedure advances to a second stage, where …

Applications Of Bayesian Functional Data Analysis, Zhexuan Yang

Graduate Research Theses & Dissertations

Functional Data Analysis (FDA) is a statistical approach used to analyze data that vary across a domain, such as curves or functions. This dissertation investigates Bayesian Functional Data Analysis (BFDA) through three applications. First, we explore the use of BFDA in outcome-dependent follow-up (ODFL) studies. After conducting simulation studies, we apply our model to cardiotoxicity and kidney function data. Second, we extend BFDA to genetic data by modeling DNA methylation levels with a three-parameter skew-normal distribution and an alpha-skew generalized normal distribution. This study also introduces a novel Multistage Markov Chain Monte Carlo (MMCMC) method with the goal of identifying …

Covariance Matrix Forecasting Of Equity Portfolios, Michael Nebor

Graduate Research Theses & Dissertations

This dissertation consists of two papers. The first paper introduces DCC-SVR, a hybrid Dynamic Conditional Correlation (DCC) and Support Vector Regression (SVR) method of forecasting the covariance matrix. This paper shows that DCC-SVR is able to outperform the traditional methods of DCC and rolling historical on multiple data sets. Performance is shown for both standard GARCH and GJR-GARCH methods. This paper also analyzes performance when dimensions are increased to 49 dimensions and when an application using equal weighted portfolio allocation is used.

The second paper introduces a covariance matrix forecasting method based on copula-GARCH simulated returns. The accuracy of this …

Efficient Algorithms For Nearest Correlation Matrix Computation With Missing Data, Ibrahim Eniola Oyeyinka

Graduate Research Theses & Dissertations

This thesis investigates efficient algorithms for computing the Nearest Correlation Matrix (NCM) under incomplete financial data. Correlation matrices are vital in portfolio optimization and risk management, yet empirical estimates often violate symmetry, positive semidefiniteness, and unit diagonal conditions due to missing observations. Two projection-based methods are analyzed: the Modified Alternating Projections (MAP) and Anderson Acceleration (AA). Theoretical analysis using convex optimization and normal cone characterization supports numerical evaluation on synthetic and real-world stock-return matrices (550×550, 2020–2025). Missing data are modeled through Missing Completely at Random (MCAR) and Not Missing at Random (NMAR) mechanisms. The results show that AA converges faster …

A Modern Optimization Approach With Data-Driven Analytical Modeling For The Healthcare Business Segment (Hbs) From The S&P 500, Aditya Chakraborty, Chris Tsokos

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Introduction: The S&P consists of eleven business segments, which are classified according to the type of industry. The current study focuses on developing a non-linear analytical model for the Healthcare Business Segment (HBS) of the S&P 500, as a function of different economic & financial indicators. Materials and Methods: The analytical model used six financial indicators together with four economic indicators to predict the weekly average closing price (WCP) of HBS stocks. Johnson’s SB transformation corrected skewness, while desirability-based optimization identified indicator values maximizing WCP. The model’s performance and generalizability were validated through repeated 10-fold cross-validation. Results: All attributable contributors …

Estimating The Gender Wage Gap: A Comparative Analysis Of Different Estimators, Xinran Zhang

Mathematics, Statistics, and Computer Science Honors Projects

The gender wage gap between males and females has been well studied by labor economists. We take a multi-prong approach to evaluate three estimators —a regression-imputation estimator, a weighting estimator, and a doubly robust estimator—in estimating the gender wage gap. Using the Panel Study of Income Dynamics, we conduct an empirical study of the estimators’ performances. In a simulation study, we evaluate the properties of estimators and study whether bootstrapping is an appropriate measure of the uncertainty of each estimator. The findings show that while the estimators provide different results, the doubly robust estimator provides reliable and consistent results under …

Action This Day: The Mathematics And Machinations That Bested The German Enigma, Jonah Weinbaum

Dartmouth College Master’s Theses

This thesis presents a comprehensive and chronological overview of cryptographic techniques designed to break Enigma, beginning in 1932 and culminating in the creation of the Turing-Welchman Bombe. We discuss the mathematical theory and electromechanical implements used to decode one of history's greatest ciphers.

Reexamining the Bombe through the lens of modern group theory, we critique Alan Turing's estimation of the number of "stops" that the Bombe produces for various plaintext-ciphertext pairing structures. To address its limitations, we introduce a new framework for estimating the number of stops by extending John Dixon's theorem concerning the probability that uniformly distributed elements of …

Food Insecurity Among Graduate Students At The University Of Alabama At Birmingham, Amy Elizabeth Callahan

All ETDs from UAB

College food insecurity (FI) has grown steadily as a field of research over the past fifteen years. Existing research primarily has demonstrated that FI is a persistent problem with various risk factors and myriad potential impacts on students, though graduate students are largely understudied in favor of undergraduate and aggregated student populations. Given the unique circumstances graduate students face, as well as expected demographic differences such as age and household characteristics, this is a serious gap in the literature. This dissertation aims to investigate graduate student food insecurity in the United States, and particularly at the University of Alabama at …

Bayesian Networks For Safety-Critical Systems, Joseph Mietkiewicz

Theses

This thesis addresses a operational challenge in modern industrial operations: the increasing complexity of systems and the consequent cognitive burden on operators. As industrial technologies advance, the human-computer interface has become the primary conduit for information flow, playing a pivotal role in operational decision-making. However, the proliferation of data often leads to information overload, potentially compromising rather than enhancing operator performance. This research explores an approach to this pressing issue through the application of Bayesian networks as decision support systems in safety- critical scenarios. Our study employs a multi-faceted approach, combining theoretical modeling with empirical testing. Through collaboration with industry …

Optimal Data Splitting Methods, Sujay Mudalgi

Theses and Dissertations

In predictive modeling, effective data splitting is crucial for creating statistically representative training and validation sets. The state-of-the-art data splitting methods are based on minimizing the energy distance between the split subsets. However, there are a number of limitations in the existing methods, which this dissertation aims to address. First, the existing methods were computationally inefficient. Thus, Chapter 2 proposes a method to scale up these approaches for big data. Here, we introduce scalable Twinning (s-Twinning), which significantly improves the execution speed of data splitting without sacrificing accuracy. Second, the existing methods did not consider the predictive relationship in the …

Machine Learning Models Leveraging Patient-Similarity And Clinical Temporality For Disease Prognoses, Ahmad F. Al Musawi

Theses and Dissertations

Electronic Health Records (EHRs) constitute a comprehensive and high-dimensional repository of clinical data, encompassing a wide array of patient-level information such as diagnoses, procedures, medications, laboratory results, and unstructured clinical narratives. These data hold immense potential for advancing predictive modeling in healthcare, including tasks such as disease progression modeling, hospital readmission prediction, and length of stay (LoS) estimation. However, the intrinsic complexity of EHR data—manifested in its heterogeneity, sparsity, and temporal dynamics—poses significant analytical challenges that limit the generalizability and interpretability of conventional machine learning models. Recent methodological advancements in deep learning and graph-based learning, particularly Graph Neural Networks (GNNs), …

Climate Migration And Urban Survival: Evidence From Chittagong’S Slums, Mohammad Nur Nobi

Graduate Research Theses & Dissertations

This study examines the impact of climate change on rural-to-urban migration in Chittagong, Bangladesh. Based on a primary survey of 400 respondents across 35 slums in the country's second-largest city, the analysis employs two estimation methods: a multinomial logit model to assess the influence of climate-related factors on migration decisions, and a logit model to evaluate the impact of migration on the living conditions of migrants. The results show that individuals involved in ‘agriculture and daily labor’ are most likely to migrate. Compared to the base category (‘Other Reasons’) for migration, the odds ratios for ‘floods’, ‘droughts’, and ‘better job …

Effect Of Universal Health Coverage On Neonatal Mortality Rate In Sub-Saharan Africa: Addressing Missing Data In Health Indicators, Elizabeth Nalule Arihoona

Graduate Research Theses & Dissertations

This study utilizes two mixed-effects models to examine the effect of Universal Health Coverage on Neonatal Mortality Rate (NMR) in 30 countries of Sub-Saharan Africa from 2000-2019 while addressing the missing data in health indicators. Using natural cubic spline interpolation, I impute the missing values in the health coverage indices to preserve the location- specific trends of the data. Findings indicate that among the health Coverage indices, only Index1, which directly relates to coverage of maternal, newborn and child health services is significantly associated with reduced NMR. While higher Current Health Expenditure is associated with reduced NMR, a higher share …

Unlocking The Secrets Of High-Frequency Financial Data: Innovative Approaches To Modeling And Analysis, Lu Zhang

Graduate Research Theses & Dissertations

High-frequency trading (HFT) has emerged as a pivotal innovation in modern financial markets, characterized by rapid, data-driven decision-making processes that capitalize on granular, time-stamped market data. This dissertation examines the predictability of intraday stock price movements during the final 30 minutes of U.S. trading, employing a Bayesian regression model with Student-t error terms to address the limitations of traditional Gaussian-based methods. The analysis reveals a decline in established predictors and the emergence of new dynamics, such as post-Federal Reserve "tug-of-war" effects. The research further advances high-frequency financial modeling by introducing a comprehensive data engineering pipeline and applying cutting-edge deep learning …

Linking Empirical Data And Numerical Simulation To Characterize Dynamic Fire Behavior Associated With Interacting Firelines, Marta Sergeevna Jerebets

Graduate Student Theses, Dissertations, & Professional Papers

Understanding fuel pattern-fire process relationships is key for predicting fire behavior and effects with follow-on benefits to proactive fire management and model validation. To characterize dynamic fire behavior, this thesis leverages empirical data and numerical simulation through two complementary studies.

In the first study, longwave thermal sensors aboard unmanned aerial systems (UAS) were used to capture fine-scale fire behavior in two experimental grass burns. A novel paired design was used to quantify the effects of fuel arrangement on fire behavior with 3.66 m diameter treatments cut to a height of 0.15 m. The treatments ephemerally reduced fire rate of spread …

A New G Family: Properties, Characterizations, Different Estimation Methods And Port-Var Analysis For U.K. Insurance Claims And U.S. House Prices Data Sets, Ahmad M. Aboalkhair, Gholamhossein Hamedani, Nazar Ali Ahmed, Mohamed Ibrahim, Mohammad A. Zayed, Haitham M. Yousof

Mathematical and Statistical Science Faculty Research and Publications

This paper introduces a new class of probability distributions, termed the generated log exponentiated polynomial (GLEP) family, designed to enhance flexibility in modeling complex real financial data. The proposed family is constructed through a novel cumulative distribution function that combines logarithmic and exponentiated polynomial structures, allowing for rich distributional shapes and tail behaviors. We present comprehensive mathematical properties, including useful series expansions for the density, cumulative, and quantile functions, which facilitate the derivation of moments, generating functions, and order statistics. Characterization results based on the reverse hazard function and conditional expectations are established. The model parameters are estimated using various …

A Bayesian Complex-Valued Latent Variable Model Applied To Functional Magnetic Resonance Imaging, Chase J. Sakitis, D. Andrew Brown, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

In linear regression, the coefficients are simple to estimate using the least squares method with a known design matrix for the observed measurements. However, real-world applications may encounter complications such as an unknown design matrix and complex-valued parameters. The design matrix can be estimated from prior information but can potentially cause an inverse problem when multiplying by the transpose as it is generally ill-conditioned. This can be combat by adding regularizers to the model but does not always mitigate the issues. Here, we propose our Bayesian approach to a complex-valued latent variable linear model with an application to functional magnetic …

Bayesian Merged Utilization Of Grappa And Sense (Bmugs) For In-Plane Accelerated Reconstruction Increases Fmri Detection Power, Chase J. Sakitis, Daniel B. Rowe

Mathematical and Statistical Science Faculty Research and Publications

In fMRI, capturing brain activity during a task is dependent on how quickly the k-space arrays for each volume image are obtained. Acquiring the full k-space arrays can take a considerable amount of time. Under-sampling k-space reduces the acquisition time, but results in aliased, or “folded,” images after applying the inverse Fourier transform (IFT). GeneRalized Autocalibrating Partial Parallel Acquisition (GRAPPA) and SENSitivity Encoding (SENSE) are parallel imaging techniques that yield reconstructed images from subsampled arrays of k-space. With GRAPPA operating in the spatial frequency domain and SENSE in image space, these techniques have been separate but can …

Investigating Risk Of Suboptimal Hiv Treatment Outcomes (Treatment Failure And Disengagement) Among Pregnant And Postpartum Women Living With Hiv, Kevin Owuor

All ETDs from UAB

The joint United Nations Programme on HIV/AIDS (UNAIDS) has set targets to end the HIV epidemic by improving HIV diagnosis, treatment, and viral suppression to reduce new infections and improve health outcomes for people living with HIV (PLHIV), including pregnant and postpartum women living with HIV (PPWH). After diagnosis, linking PPWH to antiretroviral (ART) treatment is critical to suppress the virus and prevent mother-to-child transmission (MTCT) of HIV. Continuous engagement in care is essential for achieving and maintaining virologic suppression in the peripartum and postpartum period for PPWH, both for their own health and the health of their family. Disengagement …

Early Entering Research And Writing (Eerw) In College-Level Statistics, Rebecca Fang

2025 Faculty Bibliography

With the high demand for data-driven knowledge, students today need to comprehend skills in problem-solving, critical thinking, communication, and collaboration. However, in traditional statistics lectures, students learn descriptive algorithms, hypothesis, and linear regression at their early stage of college. Unfortunately, they often analyze numbers without considering the context, purpose, audience, and the meaning of the numbers. Therefore, providing students the early opportunity to research and write with data in college will allow them to deepen their understanding of the subjects, to think critically about the data and the results, to communicate effectively with diverse audience, and to prepare for their …

Group And State-Specific Estimation In Animal Movement Models Using A Bayesian Approach, Anita Bhandari Sharma

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This research study investigates statistical approaches for modeling the movement patterns of white-tailed deer in Louisiana using GPS tracking data. We start by classifying the latent behavioral states using a hidden Markov model (HMM) and then integrate those inferred states into a state-dependent step selection framework to evaluate the land cover preferences. Standard HMMs, however, assume the same movement patterns for all animals, overlooking the differences due to characteristics such as sex, age, breeding season, etc.

To address this limitation, we extend the modeling framework to incorporate the group-level structure defined by similar characteristics or conditions to assess whether such …

Check Your Data Before You Wreck Your Model: The Impact Of Careless Responding On Substance Use Data Quality, Abby L. Braitman, Anna M. Petrey, Jennifer L. Shipley, Rachel Ayala Guzman, Emily Renzoni, Alison Looby, Adrian J. Bravo

Psychology Faculty Publications

Background: The accuracy of survey responses is a concern in research data quality, especially in college student samples. However, examination of the impact of removing participants from analyses who respond inaccurately or carelessly is warranted given the potential for loss of information or sample diversity. This study aimed to understand if careless responding varies across a number of demographic indices, substance use behaviors, and the timing of survey completion.

Method: College students (N = 5809; 70.7% female; 75.7% White, non-Hispanic) enrolled in psychology classes from six universities completed an online survey assessing a variety of demographic and substance use-related information, …

Analysis Of Public Acceptance Of Urban Air Mobility (Uam) Based On Air Travel Frequency, Seuggyun Jin, Kim O. Chambers

Journal of Aviation/Aerospace Education & Research

Urban Air Mobility (UAM) is an innovative air transportation system designed for efficient travel in urban and suburban areas, offering significant time saving compared to traditional ground transportation. However, concerns about UAM services, such as safety and noise, remain prominent. Understanding public acceptance of UAM is crucial to identifying potential customers and ensuring the sustainability of commercial UAM operations. This study utilizes an online survey from a total of 254 consumer attitudes on the scales of reliability, usefulness, behavioral intention, safety, and concerns to examine public acceptance of UAM based on people's air travel frequencies. Using a one-way ANOVA, the …

Analysis Of Cytokine Data In A Case-Control Study Of Me/Cfs, Navya Amaratunga

Electronic Theses & Dissertations (2024 - present)

This study investigated the role of cytokines in Myalgic Encephalomyelitis/Chronic Fatigue Syndrome (ME/CFS), a debilitating multi-system disease with unknown etiology. Using data from a case-control study (60 cases, 61 controls) previously collected by Dr. Roxana Moslehi’s lab, serum levels of 48 cytokines were analyzed. Statistical analyses, including T-tests, Wilcoxon Rank Sum tests, and logistic regression (unadjusted and adjusted for medication use), were performed to identify differences between cases and controls.

Results revealed a statistically significant difference in mean Fractalkine levels between cases and controls (p=0.031). Borderline significant differences were also observed for IL-6 (p=0.046) and MIP-1α (p=0.046). Unadjusted logistic regression …

Group Antenatal Care Positively Transforms The Care Experience: Results Of An Effectiveness Trial In Malawi, Crystal L. Patil, Kathleen F. Noor, Esnath Kapito, Li C. Liu, Xiaohan Mei, Elizabeth Chodzaza, Genesis Chorwe-Sungani, Ursula Kafuulafula, Elizabeth T. Abrams, Allissa Desloge, Ashley Gresh, Rohan D. Jeremiah, Dhruvi R. Patel, Anne Batchelder, Heidy Wang, Jocelyn Faydenko, Sharon S. Rising, Ellen Chirwa

Department of Medicine Faculty Publications

Background

We developed and tested a Centering-based group antenatal (ANC) model in Malawi, integrating health promotion for HIV prevention and mental health. We present effectiveness data and examine congruence with only the Group ANC theory of change model, which identifies key processes as supportive relationships, empowered partners in learning and care, and meaningful services, leading to better ANC experiences and outcomes.

Methods

We conducted a hybrid effectiveness-implementation trial at seven clinics in Blantyre District, Malawi, comparing outcomes for 1887 pregnant women randomly assigned to Group ANC or Individual ANC. Group effects on outcomes were summarized and evaluated using t-tests, Mann-Whitney, …

Modeling Non-Normal Distributions With Mixed Third-Order Polynomials Of Standard Normal And Logistic Variables, Mohan D. Pant, Aditya Chakraborty, Ismail El Moudden

Epidemiology, Biostatistics, & Environmental Health Faculty Publications

Continuous data associated with many real-world events often exhibit non-normal characteristics, which contribute to the difficulty of accurately modeling such data with statistical procedures that rely on normality assumptions. Traditional statistical procedures often fail to accurately model non-normal distributions that are often observed in real-world data. This paper introduces a novel modeling approach using mixed third-order polynomials, which significantly enhances accuracy and flexibility in statistical modeling. The main objective of this study is divided into three parts: The first part is to introduce two new non-normal probability distributions by mixing standard normal and logistic variables using a piecewise function of …