Statistics and Probability Commons^™

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 …

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 …

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 …

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 …

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 …

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, …

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.

Sexually Transmitted Infection (Sti) Incidence And Risk Factors Among People With Hiv (Pwh): Insights From A 13-Year Cohort Study In South Carolina, Salome-Joelle Gass, Shujie Chen, Jiajia Zhang Ph.D., Bankole Olatosi

Faculty Publications

The Ending the HIV Epidemic (EHE) initiative aims to reduce new HIV infections by 90% by 2030 in the United States (US). However, rising sexually transmitted infection (STI) rates exacerbate the bidirectional infection risk between HIV and STIs. Most research on STIs among people with HIV (PWH) has focused on high-risk groups, resulting in limited data on broader populations. This study addresses that gap by examining the incidence and risk factors for gonorrhea, chlamydia, and syphilis in a statewide cohort of PWH in South Carolina. Data from South Carolina’s HIV and STI surveillance systems were linked, and all PWH aged …

Analyzing The Sentiment Of Feminist And Non-Feminist Works, Jasmine Borie, Megan G. Falschlehner

Mathematics, Computer Science & Statistics Presentations

This presentation focuses on a group of texts that advocate for a change in the current belief system. These texts are the Feminist Manifesto, Sojourner Truth: Ain’t I a Woman?, and Civilization and Its Discontents. These first two texts advocate for women’s rights, while Freud’s book is focused on civilization’s decline and how our understanding of community can affect this. Through our presentation, we want to examine the differences in sentiment and language between the feminist texts and Freud’s texts to pinpoint whether or not sentiment changes when advocating for different beliefs.

A Text Mining And Sentiment Analysis Of Valuable Cie Texts Using R, Eric Sugarman, Ethan Turber-Ortiz, Hannah Quinn

Mathematics, Computer Science & Statistics Presentations

The purpose of this project was to perform a sentiment analysis of three texts used in Ursinus College's Common Intellectual Experience (CIE) course: Between the World and Me by Ta-Nehisi Coates, The New Jim Crow by Michelle Alexander and Discourse on Method by Rene Descartes. Word count and word cloud analysis were also performed on the texts as well as term frequency and bigram analysis.

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 …

Environmental Risk And Alpha-Gal Syndrome (Ags) In The Mid-Atlantic United States, Brandon D. Hollingsworth Ph.D., Margaret Wiener, Dana A. Giandimenico, Scott P. Commins, Ross M. Boyce

Faculty Publications

Alpha-gal syndrome (AGS), commonly referred to as the tick bite red meat allergy, has been reported worldwide with the number of suspected cases in the United States increasing from 24 in 2009 to over 34,000 in 2019. Within the US, AGS is associated with the bite of two tick species, Amblyomma americanum and Ixodes scapularis, and has particularly high incidence rates in the mid-Atlantic region. Because AGS is associated with tick bites, the risk of developing AGS is affected by the environment individuals visit. Despite this, as well as the numerous studies associating the environment with Am. americanum, …