Digital Commons Network^™

Novel Statistical Methods For Mediation Analysis With High-Dimensional Omics Mediators, Zhichao Xu

Dissertations and Theses (Open Access)

Mediation analysis is a widely used statistical method for examining how molecular traits, such as gene or protein expression, act as intermediaries linking an exposure to a health outcome. For example, it can help explain how smoking affects disease risk through molecular changes. The rapid progress in high-throughput omics profiling technologies and large-scale epidemiology consortia, such as the Trans-Omics for Precision Medicine (TOPMed) program from the National Heart, Lung and Blood Institute (NHLBI) and UK Biobank, now has resulted in an extensive accumulation of genomic data for biomedical research and analysis. At the same time, it poses significant methodological challenges, …

Identifying And Characterizing Transition Cells In Developmental Processes From Scrna-Seq Data, Yuanxin Wang

Dissertations and Theses (Open Access)

During the development of multicellular organisms, individual cells make distinct decisions about their cell types and states. Understanding the molecular mechanisms underlying cellular state transitions at different developmental stages provides deep insights into physiology, morphology and the etiology of diseases. Single-cell RNA-sequencing (scRNA-seq), which is widely used to study complex cell states and dynamic gene expression patterns, enables us to investigate molecular mechanisms of cellular state transitions. Currently, however, computational tools available for identifying cellular states and state transitions remain limited.

Although trajectory-based methods such as Monocle and Slingshot assume that state transitions generate continuous expression profiles, they cannot distinguish …

Using Gaussian Process Regression To Learn Thermodynamic Equations Of State With Uncertainty Quantification, Austen T. Lee

Chemical Engineering Undergraduate Honors Theses

This study investigates the use of derivative-informed Gaussian Process (GP) models to estimate thermodynamic behavior across temperature and density by building a Helmholtz-based equation of state. Argon, a stable monatomic gas, was chosen as a case study within the vapor region. The GP model was trained using values of experimentally measurable properties found by taking first and second derivatives of the original potential function. Results show that while the GP model offered uncertainty quantification and informed thermodynamic behavior, it predicted values that deviated from the ground truth depending on the property. The model exhibited high confidence in regions with substantial …

Toward The Application Of Natural Language Processing In Electronic Health Record Analysis For Taxonomy Development, Latoya Mcdonald

All Dissertations

Electronic health records (EHRs) are pivotal resources for nurse practice because they increase the timeliness and reliability of patient information at the point of care and support access by multiple healthcare providers and the individual patients themselves. However, it is widely recognized that data extraction from EHRs is challenging due to the variability in the language used in clinical care notes and the lack of standardized terminology across healthcare systems. The broad objective of this dissertation is to develop taxonomy-based classification models for nursing care by applying feature engineering approaches to EHRs that include nursing care of ostomy patients following …

A System And Method For Measuring Spatially Varying Surface Appearances With A Study Of Feathers, Jessica Baron-Lis

All Dissertations

Real-world materials, particularly biological structures such as feathers exhibit complex appearances that vary spatially across their surfaces. The field of computer graphics provides a means of understanding such surfaces through material modeling which uses both analytical models and data acquired from light-surface interactions. There are many efforts within the past decade in measuring materials for graphics, but common limitations in these works include not accounting for spatially varying properties and reliance on neural networks and synthetic datasets.

Feathers from modern birds present diverse appearances due to how light interacts with their unique hierarchical microstructures. Variations in those structures lead to …

Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister

All Graduate Reports and Creative Projects, Fall 2023 to Present

Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI …

Kernel Density Estimation And Convolution, Nicholas Tenkorang

Open Access Theses & Dissertations

Kernel Density Estimation (KDE) is a widely used technique for estimating the probability density function of a random variable. In this study, we revisit KDE through the lens of convolution and extend this perspective to special cases such as positive, bounded and heavy tailed random variables. Building on this foundation, we propose a novel simulation-based density estimation method that generates new data by adding noise to observed values and then smoothing the resulting histogram using splines. A minor adjustment to natural cubic splines is required to ensure nonnegative estimates. The noise is drawn from a class of bounded polynomial kernel …

Optimal Accelerated Life-Testing Plans Under Progressive Type Ii First Failure Censoring Scheme, Emmanuella Duah

Open Access Theses & Dissertations

Designing optimal accelerated life testing (ALT) plans under progressive Type-II first failure censoring involves complex computational challenges, especially when trying to balance efficiency, precision, and practicality. This research introduces a novel optimization framework aimed at determining the best test configuration that minimizes estimation uncertainty within constrained experimental conditions. We developed a tailored meta-heuristic strategy based on an enhanced Variable Neighborhood Search (VNS) algorithm, which efficiently navigates the complex landscape of censoring schemes and stress allocations. Unlike conventional methods that typically focus on simpler censoring or stress-level structures, this approach simultaneously optimizes both the censoring points and sample allocations across various …

Optimal Experimental Plan For Multi-Level Stress Testing Under Progressively Hybrid Censoring, David Kojo Amakye

Open Access Theses & Dissertations

Reliability analysis is essential for understanding how products perform over time, particularly in environments where failure data is limited or costly to obtain. One effective approach is multi-level stress testing, where test units are subjected to varying levels of stress to accelerate failures and extract more information within constrained timeframes. This study presents a novel optimization-based framework for designing life-testing experiments under progressively Type-II hybrid censoring, assuming Weibull lifetime distributions. Leveraging a Variable Neighborhood Search (VNS) algorithm, we determine efficient allocations of test units and censoring parameters across multiple stress levels to enhance the precision of estimates of the model …

A Profile Wald Test In M-Estimation, Reagan Kesseku

Open Access Theses & Dissertations

Despite the growing popularity of machine learning-based inference, classical statistical inference remains highly relevant in modern data science due to its interpretability and theoretical rigor. Among its core tools, the likelihood ratio test, Wald test, and score test are foundational methods for hypothesis testing within the maximum likelihood framework. Although these tests are asymptotically equivalent under regularity conditions, each offers distinct advantages depending on the context, computational demands, and the availability of parameter estimates. In this dissertation, we introduce a fourth method, the Profile Wald Test (PWT), within the broader M-estimation framework. The PWT is based on profile estimators of …

Computing Optimal Multi-Level Stress Testing Plans Using A Combined Variable Neighborhood Search Algorithm Under Progressive Type-Ii Censoring Scheme, Michael Obuobi

Open Access Theses & Dissertations

In multi-level stress life tests under Type-II progressive censoring, determining optimal allocation poses significant computational challenges due to the vast solution space. Efficient methods are essential for exploring the admissible censoring schemes effectively. This thesis introduces a novel meta-heuristic algorithm, the Combined Variable Neighborhood Search (CVNS), which computes optimal schemes at different stress levels simultaneously. Unlike methods focusing on marginal stress levels or one-step progressive censoring, this approach leverages a unified framework to ensure enhanced computational efficiency and solution quality. By integrating the components of the design parameters into a cohesive optimization process, the algorithm effectively reduces computational time while …

Clinicogenomic Insights For Prostate Cancer Progression, Kelvin Ofori-Minta

Open Access Theses & Dissertations

Prostate cancer (PrCa) remains a critical challenge in precision oncology due to several reasons including its apparent heterogenous condition, recurrence following treatment and rapid progressive forms. Therefore, identifying patients at risk of progression is essential to fast-track therapeutic decisions and improve outcomes. Despite recent advances in genomic and molecular profiling, conventional PrCa risk assessment tools heavily rely on a few clinical parameters, neglecting the prognostic potential of genomic biomarkers in the presence of clinical biomarkers. This study presents a computational pipeline to harmonize and evaluate the prognostic value of clinicogenomic profiles of patients in modelling progression free survival (PFS). PFS, …

Development Of Saddlepoint Methodologies For Sparse Sample Multiple Parameter Generalized Linear Models And Correlated Data Scenarios, Christopher Johnson

All Graduate Theses and Dissertations, Fall 2023 to Present

Parameter estimation using maximum likelihood techniques may be biased when sample sizes are small, event rates are low, or otherwise sparse counts exist in a parametric model. This in turn may lead researchers to draw invalid statistical conclusions when conventional methods are utilized. The saddlepoint approximation has potential to lessen the degree of bias in sparse data conditions through its use of moments beyond the mean and variance, which allows for more accurate approximations using a smaller number of observations. We propose two novel saddlepoint methods for use in practical analysis scenarios, as an alternative to maximum likelihood estimation. First, …

Computing Optimal Progressive Hybrid Censoring Schmes Using An Mcmc Type Probabilistic Approach, Irene Yemotiorkor Odoi

Open Access Theses & Dissertations

Progressive hybrid censoring schemes play a crucial role in optimizing life-testing experiments by balancing test duration and statistical efficiency. This study presents a Markov Chain Monte Carlo (MCMC)-based probabilistic approach for determining optimal progressive hybrid censoring schemes, incorporating a time-dependent component that enhances traditional progressive censoring methods.We implement our approach using three distinct probability distributions the multinomial, hypergeometric, and uniform distributions to simulate censoring schemes. The optimal censoring schemes are then identified based on three optimality criteria: A-optimality, D-optimality, and T-optimality, ensuring robust selection by minimizing estimator variance, maximizing Fisher information, and optimizing test duration, respectively..To evaluate the effectiveness of …

Yield Prediction Of Pv Solar Energy Systems And Its Application In The Energy Grid For Operational Efficiency, Pablo Bustamante

Open Access Theses & Dissertations

The generation of power from photovoltaic (PV) solar panels is influenced by a multitude of factors. These include the tilt and orientation of the solar panels, the latitude of their location, and the prevailing climate and weather conditions. Additionally, shading at specific locations, particularly if the panels are not part of a solar facility, can significantly impact their efficiency. The quality and efficiency of the panels themselves, along with the preventive maintenance of both the solar panels and associated components such as inverters and trackers, are also critical. Finally, the overall system design and installation play a vital role in …

Exploring Latent Mediation Through Bayesian Regularization Methods Of Lasso, Ridge, Horseshoe, Spike-And-Slab, Ethan Harris

Graduate Theses and Dissertations

Regularization is a powerful tool to combat overfitting and drive sparsity in complex models. Regularization was initially applied in regression modeling but has been increasingly utilized in structural equation modeling where its utility in identifying the essential components has helped improve modeling. As structural equation models have increased in complexity both in the number of indicators but also the number of latent factors, researchers have begun to investigate how applying Bayesian regularization to these systems can further push the limits on modeling complex models with limited sample sizes. One area where research is limited is the application of Bayesian regularizations …

Latent Variable Dyadic Regression Models For Predicting Over/Under Bets In Sports Betting, Alexcia Trejo

Graduate Theses and Dissertations

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the …

Correcting Sampling Bias With Privacy-Preserving Synthetic Data: Inference Stability Under The Da-Mi Framework, Hannah Jiang

Arts & Sciences Graduate Student Theses and Dissertations

With the rapid advancement into the Data Age, synthetic data has emerged as a promising avenue for sharing scientific information while protecting the original data. While existing research has primarily focused on generating synthetic data to accurately replicate the characteristics of observed data, we explore the potential of synthetic data to adjust for unrepresentative sampling. This study explores how sampling bias—specifically unbalanced subsets—impacts statistical inference, and whether synthetic data can help correct such bias. Using a Data Augmentation–Multiple Imputation (DA–MI) framework, we generate synthetic datasets from biased samples and evaluate parameter recovery under different correction strategies. Simulations under a Missing …

Analyzing Sleep Staging And Cognitive Transitions Using Fmrl Time Series Analysis, Qingwen Zeng

Arts & Sciences Graduate Student Theses and Dissertations

Understanding sleep stages and their underlying cognitive transitions is essential for advancing our knowledge of brain function. While sleep staging has traditionally relied on electroencephalogram (EEG), this study explores the feasibility of classifying sleep stages using only functional Magnetic Resonance Imaging (fMRI) data. We investigate the use of Hidden Markov Models (HMM) and Hierarchical Dirichlet Process Hidden Markov Models (HDP-HMM) to capture temporal brain-state dynamics from task-free fMRI recordings. A series of pipelines—including sliding window, two-stage majority filtering, and prototype-based mapping via the Hungarian algorithm—were implemented to align model-inferred states with EEG-defined sleep stages. Among the twelve tested pipelines, the …

The Little Diagram That Could: Geometric Properties And Statistical Applications Of Persistence Diagrams In Topological Data Analysis, Eugene Kler

McKelvey School of Engineering Graduate Student Theses & Dissertations

Topological Data Analysis (TDA) is a collection of techniques for data analysis that leverages topological invariants of spaces formed from data points. These methods excel at extracting useful information from noisy or sparse data, making them attractive to many mathematicians, statisticians, and scientists. In this thesis, we explore TDA on three fronts: algebraic foundations, statistical applications, and metric properties. Throughout, the central object of study is the Persistence Diagram (PD), a summary of the changes in homology that occur as one builds simplicial complexes from the data by increasing a parameter.

Efficient Fully Bayesian Approach To Brain Activity Mapping With Complex-Valued Fmri Data, Zhengxin Wang, Daniel B. Rowe, Xinyi Li, D. Andrew Brown

Mathematical and Statistical Science Faculty Research and Publications

Functional magnetic resonance imaging (fMRI) enables indirect detection of brain activity changes via the blood-oxygen-level-dependent (BOLD) signal. Conventional analysis methods mainly rely on the real-valued magnitude of these signals. In contrast, research suggests that analyzing both real and imaginary components of the complex-valued fMRI (cv-fMRI) signal provides a more holistic approach that can increase power to detect neuronal activation. We propose a fully Bayesian model for brain activity mapping with cv-fMRI data. Our model accommodates temporal and spatial dynamics. Additionally, we propose a computationally efficient sampling algorithm, which enhances processing speed through image partitioning. Our approach is shown to be …

Sabrina Vs Steph: The Battle Between The Wnba And Nba, Naysha Mcgriff

Symposium of Student Scholars

The average salary of a Women’s National Basketball Association (WNBA) player is 110 times less than a National Basketball Association (NBA) player’s. Despite growing WNBA viewership, gender inequality in sports remains high, with critics claiming female athletes are less skilled. Gender bias in sports is severely understudied, making direct comparisons to men’s leagues unfair due to long-term lack of investment in women’s sports. This study investigates whether the perceived disparity in skill levels between WNBA and NBA players' is genuine or influenced more by external factors by developing an unbiased measure of player efficiency to compare athletic performance. This dataset …

Opioid Epidemic In Maine: An Analysis Of Increasing Overdose-Related Deaths Following The Coronavirus, Aysel S. Hamlin

Thinking Matters Symposium

The rate of drug overdose resulting in death doubled in Maine following the COVID-19 pandemic from the onset of the COVID-19 pandemic in late 2019 through 2022. The correlation between increased isolation during the pandemic and overdose death rates sheds a concerning light on the insufficient resources for people struggling with Opioid Use Disorder (OUD) throughout Maine. The increasing trade and access to fentanyl following the pandemic accounted for the majority of drug-related deaths in Maine in 2021 and 2022. This study examines the need for long-term access to drug treatment in rural and urban Maine, both environments with varying …

Mortgage Default Classification Modeling For Variable Analysis, Brendan R. Goggins

Honors College Theses

The financial crisis of the early 2000’s is a prime example of the severe consequences that mortgage default and borrower insolvency can have on economies at large. Mortgage default specifically is a prime case with the popularization of mortgage backed securities and the commonality of this loan structure. Multiple hypotheses and models have been formed to understand the reasons, causes, and consequences of mortgage default. This paper uses both machine learning and statistical classification models to inform an understanding of the variables most significant and impactful to the default outcome of mortgages. Consideration is given to both loan-level microeconomic variables …

New Deep Learning Approaches To Classical Statistical Problems, Shijie Wang

Theses and Dissertations

The field of deep learning (DL) has received considerable attention in recent years. Thanks to rapid growth in computational power, the ability to collect massive datasets, and improvements in software and algorithms, DL is now routinely applied to areas as diverse as computer vision, natural language processing, and bioinformatics. At the same time, DL is only starting to be explored in the context of classical statistical inference problems such as bootstrapping, quantile regression, and mixture modeling. In this dissertation, we develop new DL methodology for three classical statistical problems: 1) weighted M-estimation, 2) joint quantile regression, and 3) mixing density …

Who Am I As A Learner? The Effects Of A Self-Regulated Learning Intervention On Ninth Graders’ Academic Self-Concepts And Intrinsic Motivation, Kaitlyn Elizabeth Bailey

Theses and Dissertations

The transition from middle school to high school is a notoriously difficult one, and a student’s success in ninth grade is typically a predictor of future success in high school. Unfortunately, many ninth graders struggle to engage in school. Despite their capability to complete the work, these students appear unmotivated and simply disengaged from school. In this action research case study, three students had the opportunity to share their education stories and the experiences that have shaped their academic self-concepts. These students also participated in a self-regulated learning intervention to determine if students’ feelings of autonomy, competence, and relatedness could …

Bayesian Joint Modeling Of Longitudinal Data And Interval-Censored Failure Time Data, Yuchen Mao

Theses and Dissertations

Longitudinal data are a collection of repeated observations of the same subjects at different points in time. Interval-censored data arise when the time to the event of interest for each subject is never exactly observed but known to fall between two consecutive points in time. Joint analysis of longitudinal data and interval-censored failure time data can lead to more accurate estimates compared to separate modeling when the correlation among events of interest or intracluster correlation is present. The aim of this dissertation is to develop efficient and reliable joint analyses of longitudinal and interval-censored failure time data using Bayesian methods. …

The Impact Of Institutional Features On Student Retention Rates Using Regression And Random Forest Modeling, Grayson Tvrdik

Honors Projects

Student retention is a focus for higher education institutions aiming to improve student outcomes and institutional success. While previous research has often relied on qualitative assessments of college related factors, this project applies quantitative techniques at a national scale. Random forest and beta regression models were used to predict retention rates for public colleges based on institutional characteristics such as financial variables, enrollment patterns, and demographic metrics. The random forest models demonstrated higher accuracy than the beta regression models, leading us to find that financial variables and student integration factors are significant predictors of retention. Beta regression models, though less …

Changes In The Incidence, Viral Coinfection Pattern And Outcomes Of Pneumococcal Hospitalizations During And After The Covid-19 Pandemic, King-Pui Florence Chan, Ting Fung Ma, Hanshu Fang, Wai-Kai Tsui, James Chung-Man Ho, Mary Sau-Man Ip, Pak-Leung Ho

Faculty Publications

Background

The incidence of pneumococcal pneumonia in the context of the Coronavirus Disease 2019 (COVID-19) pandemic, along with the real-world data on the ratio of non-invasive to invasive pneumococcal pneumonia, is an area that has not been thoroughly studied. The outcomes associated with coinfection of influenza and COVID-19 remain unknown. This study examined the incidence, demographics, coinfection with influenza and/or COVID-19, and clinical outcomes of pneumococcal hospitalizations in Hong Kong during the baseline, pandemic, and post-pandemic periods.

Methods

Hospitalization records of individuals aged 18 years and above with pneumococcal disease from January 2015 to August 2024 were extracted from the …

Representative Sampling, Jessica Choe, Lisa K. Lashley, Charles J. Golden

Essays in Developmental Psychology

The research process begins with an initial observation that scientists or researchers of various backgrounds want to engage in and understand, which prompts them to formulate theories and hypotheses, collect data, and use statistical procedures to organize, summarize, and interpret gathered data. Research conducted through questionnaires or surveys are often utilized to determine the nature of a population or the interests of members in a particular group.