Statistics and Probability Commons^™

Association Between Lifetime Interpersonal Violence And Post– Covid-19 Condition Among Women In Kentucky, 2020-2022, Ayşe Güler, Heather M. Bush, Katie Schill, Nurlan Kussainov, Ann L. Coker

Biostatistics Faculty Publications

Objective: The COVID-19 pandemic increased the risk of interpersonal violence. We investigated the association between lifetime interpersonal violence experience and risk of post–COVID-19 condition (the persistence of symptoms of COVID-19 and severity of health problems associated with COVID-19 that last a few weeks, months, or years) among women with lifetime interpersonal violence experience.   Methods: Women participants aged ≥18 years in Kentucky’s Wellness, Health & You—COVID-19 study completed online quantitative surveys about the impacts of the pandemic, developing COVID-19, and symptoms of post–COVID-19 condition. We conducted cross-sectional analyses estimating rate ratios of developing COVID-19 and symptoms of post–COVID-19 condition during the …

Elements Of Statistics, Paul Flesher, Jeffrey Sadler, Jonathan Rehmert, Lanee Young

All Open Educational Resources

This undergraduate text, intended for an introductory statistics course, motivates and develops basic inferential statistics. The first chapter establishes the necessity of inferential statistics, lays the basis of a scientific worldview, and finishes by introducing sampling methods and variables. Subsequent chapters develop visualizations, descriptive statistics, probability, and random variables. Sampling distributions are treated in the fifth chapter which is immediately followed by the development of confidence intervals and hypothesis testing. The text concludes with a treatment of linear regression. The use of Excel is emphasized throughout. The text is best viewed online through LibreTexts.

Temporal Network Analysis Of Comorbidities Among People With Hiv In South Carolina, Yunqing Ma, Matthew Lohman Ph.D., Monique J. Brown Ph.D., Mph, Yichen Li, Xiaoming Li Ph.D., Bankole Olatosi Ph.D., Jiajia Zhang Ph.D.

Faculty Publications

Introduction: People with HIV experience a high rate of comorbidities that can complicate their health outcomes. Understanding the prevalence, interrelationships and temporal development of these comorbidities is crucial for improving health management and quality of life for people with HIV. Methods: We used a population-based cohort extracted from statewide electronic health record (EHR) data in South Carolina (SC), including 18 649 people with HIV who survived at least 1 year after HIV diagnosis between January 1, 2005, and December 31, 2020. Comorbidities and organ systems were classified using ICD-10 codes. Network analysis was performed to assess the closeness centrality among …

‘Waves Of Imagination’ Unconditional Spectogram Diffusion Using Diffusion Architecture., Rahul Vanukuri

Computer Science and Engineering Theses - Archive

The swift evolution of wireless communication technologies,particularly in the field of rf signals or in CBRS bands,demands increasingly sophisticated signal processing techniques to ensure efficient transmission, reception, and spectrum management.Traditional approaches to signal generation and reconstruction, although effective in controlled environments, often struggle to cope with the challenges presented by real-world noisy conditions, hardware constraints, and limited access to large-scale datasets. In response to these limitations, this thesis explores the application of diffusion models—a class of generative models known for their ability to produce high-fidelity samples—to the domain of spectrogram generation for communication signals.

Different from conventional strategies to simulate …

Understanding The Challenges And Satisfaction Among The Medical Professional Preceptors, Cornelia Ko

West Chester University Doctoral Projects

This study evaluates the Penn Medicine Family Medicine Clerkship program through a mixed-methods approach, exploring the challenges and factors influencing the satisfaction of medical professional preceptors when precepting students. A quantitative survey with 54 preceptor participants reveals the top challenges preceptors face: workload, conflicts with patient care, and limited clinical space. Despite this, there is strong support for the program, with 89% of preceptors enjoying the role and 85% expressing willingness to continue to precept for the next three years. Finally, data analysis shows no statistical correlation between preceptor motivation with gender, ethnicity, age group, years of serving, or total …

Highest Risk Density Region For The Communication Of The Impact Of A Treatment Covariate On The Time-To-Event Distribution, Giacomo Biganzoli, Giuseppe Marano Phd, Patrizia Boracchi Phd

COBRA Preprint Series

Analysis of time-to-event (TTE) data is central to clinical research, yet conventional summary measures like the hazard ratio (HR) and restricted mean survival time (RMST) present significant challenges. The HR is often misinterpreted, and its validity depends on the frequently violated proportional hazards assumption, while the RMST is highly sensitive to the choice of time horizon. This paper introduces two novel, assumption-free estimands to address these limitations: the Highest Risk Density Region (HRDR) and the Highest Net Risk Difference Region (HNRDR).

The HRDR identifies the narrowest time interval containing a pre-specified probability mass of events, directly answering the clinical question: …

การเปรียบเทียบการทำนายผลการแข่งขันฟุตบอลโดยใช้การเรียนรู้เชิงลึกจากการผสานข้อมูลทางสถิติและแผนที่ความร้อนของผู้เล่น, ภานุพันธุ์ พันธุ์พฤกษ์

Chulalongkorn University Theses and Dissertations (Chula ETD)

งานวิจัยนี้มีวัตถุประสงค์เพื่อเปรียบเทียบประสิทธิภาพของแบบจำลอง Multilayer Perceptron (MLP), Convolutional Neural Networks (CNN) และ Multimodal Neural Network (MMNN) ในการทำนายผลการแข่งขันฟุตบอลพรีเมียร์ลีกอังกฤษ โดยใช้ข้อมูลทางสถิติ ข้อมูลแผนที่ความร้อนของผู้เล่น และการผสานข้อมูลทั้งสองรูปแบบ ผ่านแนวทาง Early Fusion, Late Fusion และการแปลง Heatmap เป็นข้อมูลเชิงตัวเลขแบบแบ่งพื้นที่สนาม ผลการวิจัยพบว่า แบบจำลอง MMNN ที่ใช้แนวทาง Late Fusion ให้ค่าความแม่นยำที่ 55.68% ซึ่งสูงกว่า Early Fusion ที่ได้ความแม่นยำ 53.45% และ CNN ที่ใช้ข้อมูลทางสถิติร่วมกับข้อมูลตำแหน่งเชิงพื้นที่จากแผนที่ความร้อนที่แบ่งสนามออกเป็น 4 ส่วน มีค่าความแม่นยำสูงสุดที่ 63.50% และให้คะแนน Precision, Recall และ F1 Score สูงกว่าแบบจำลองอื่น ๆ นอกจากนี้ ยังได้วิเคราะห์ความสำคัญของฟีเจอร์ด้วยเทคนิค SHAP ซึ่งชี้ให้เห็นว่าฟีเจอร์ที่เกี่ยวข้องกับเกมรับของทีมเหย้า เช่น การบล็อก การเคลียร์บอล และการดวลลูกกลางอากาศ มีผลต่อการทำนายมากที่สุด ขณะที่ตำแหน่งของผู้เล่นทีมเยือนมีอิทธิพลน้อยที่สุด

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 …

Analyzing Factors Influencing Employee Turnover In Tech Companies: A Predictive Modeling Approach, Shinjon Ghosh

Theses and Dissertations

Employee turnover poses substantial challenges for technology firms, and understanding its key drivers through predictive modeling is essential for developing effective retention strategies. This study investigates factors influencing employee turnover in technology companies by implementing a predictive modeling approach on the IBM HR Analytics Employee Attrition dataset. The research aims were identifying key factors contributing to employee attrition, developing predictive models to forecast turnover risk, and analyzing interactions among significant predictors. By examining a range of features, the results highlight significant variables (Over Time, Monthly Income, Marital Status, etc.) of attrition and offer actionable insights for developing targeted employee retention …

Implementation Of Project-Based Instruction In Statistics Higher Education Courses, Jennifer A. Daddysman

Theses and Dissertations--Education Sciences

Project-based instruction is an instructional methodology based in constructivism that aligns with recommendations from the American Statistical Association for teaching statistics, including incorporating real-world data and examples, collaboration, and scaffolding (GAISE College Report ASA Revision Committee, 2016; GAISE Steering Committee, 2024; Krajcik & Blumenfeld, 2005; Tishkovskaya & Lancaster, 2012). This study examined current use of project-based instruction in United States higher education courses in statistics and the supports for and challenges to implementing this type of instruction.

This study used qualitative research methodology in the integrative pedagogy and diffusion of innovation frameworks. Voluntary response surveys were distributed via two sections …

Understanding The Impact Of The Medicaid Expansion On Hospital Length Of Stay And Emergency Department Use In Kentucky, Cameron Bushling

Theses and Dissertations--Epidemiology and Biostatistics

Three related analyses were performed to try to understand the immediate effects of Medicaid expansion on hospital systems in Kentucky. Medicaid expansion led to a large, sudden increase in the number of individuals eligible for various health care needs. This sudden increase in demand for health services lead to initial hypotheses regarding hospitals’ ability to handle this demand. The first analysis examined hospital average length of stay (LOS) using a linear mixed-effects model. Length of stay was modeled longitudinally between 2013 and 2015 to determine if significant changes in the slope of LOS could be detected after expansion (2014). Separate …

Changes In Cancer Diagnosis And Survival In The United States During The Covid-19 Pandemic, Justin T. Burus

Theses and Dissertations--Epidemiology and Biostatistics

The COVID-19 Pandemic led to global societal disruptions as political leaders and public health authorities attempted to control the spread of the newly discovered SARS- CoV-2 virus. While these measures were designed to lessen morbidity and mortality from a novel pathogen, their impact was also felt in many other, often unintended ways. The purpose of this dissertation is to use cancer surveillance research methods to examine the association between COVID-19 Pandemic-related disruptions and changes in the normal diagnosis and care of cancer in the United States.

The first two studies of this dissertation analyzed reductions in cancer diagnoses in the …

Mathematical Contributions To The Study Of Chemotaxis And Cell Signaling, Hajr Zam

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation presents results from two mathematical projects concerned with the biology of cells. Chapter 1 provides biological background and places the two mathematical problems in the context of cell signaling. The larger project, with Prof. H. Hattori on a chemotaxis model is presented in Chapters 3 and 4. Work with Prof. \'{A}. Hal\'{a}sz on a chemical reaction network system with linear multimers and two types of labels is presented in Chapter 2. The chemotaxis system describes the one-dimensional dynamics of a species of cells with two chemical species, a chemo-attractant and chemo-repellent. The goal is to analyze the behavior …

Students’ Perceptions Of Self And Peers Predict Self-Reports Of Cheating, Amber M. Henslee, Luke Settles, Sara E. Johnson, Gayla R. Olbricht

Psychological Science Faculty Research & Creative Works

Academic dishonesty and how to address it are common concerns across higher education disciplines, but engineering students admit to higher rates of academic dishonesty than other students. However, first-year students may be particularly receptive to prevention efforts. Considering self-perception, social norming, and behavioral choice theories, we hypothesized that 1.) Students who perceived themself as ethical and more knowledgeable of the consequences for misconduct would be less likely to self-report cheating and 2.) Students who perceived cheating and plagiarism to be common would be more likely to self-report cheating. For this study, freshmen engineering students (N=703) reported their self-perception, perception of …

From Data To Insight: A Machine Learning Approach In Classifying Dairy Cow Productivity Level And Identifying Important Influencing Variables, Fatkhurokhman Fauzi, Achmad Fauzan, Rhendy K P Widiyanto, Khairil Anwar Notodiputro, Bagus Sartono

Knowledge Engineering and Data Science

Identifying influential predictor variables is crucial for enhancing model interpretability in supervised classification. This study applies Permutation Variable Importance (PVI), a model-agnostic approach, to evaluate variable relevance after model fitting. Using data from the 2024 Indonesia Dairy Cow Productivity Survey, this research investigates five classification techniques: (1) Support Vector Machine (SVM), (2) Neural Network (NN), (3) k-Nearest Neighbors (kNN), (4) Naïve Bayes Classifier (NB), and (5) Logistic Regression (LR), to identify which method(s) yield the best performance based on evaluation metrics such as accuracy, sensitivity, and specificity. PVI is employed to identify the most influential predictor variables within the best-performing …

Machine Learning Methods For Intrusion Detection And Response In Network Security, Ayomide Oyemaja

College of Graduate Studies: Theses & Dissertations

Intrusion Detection Systems (IDS) play a crucial role in computer network security by identifying malicious activities and potential cyberattacks. This thesis combines machine learning and cybersecurity by applying Reinforcement Learning (RL) in intrusion detection and response using the NSL-KDD dataset.

We designed and implemented a Q-learning framework where an agent learns to classify network traffic over time by interacting with the environment and receiving rewards based on detection accuracy. We also look at the importance of feature selection and classification techniques and how effective they are in improving model performance, reducing the complexity of computation, and producing more desirable results. …

Predicting Real Estate Prices Using Deep Learning Regression Models On Socio Spatial Data, Gentle Engworo

Graduate Theses/Dissertations

ABSTRACT

Cities keep their own kind of ledger. Every block, bus stop, corner store, and year that slips by leaves a small entry about what homes are worth. That ledger is what we call socio-spatial data: simple facts about what a home is (its age), where it sits (latitude/longitude), how easy it is to get around (distance to the nearest MRT station), what’s nearby (number of convenience stores), and when it sold (transaction date). This thesis asks a practical question in that everyday language: given these common clues, can we predict home prices more accurately and explain why? Using 414 …

Evaluation Of Practical Methods To Determine If A Karst Creek Is Gaining Or Losing: Case Study Of Leith Creek, Elizabeth Jones

Graduate Theses/Dissertations

Karst landscapes are abundant in Missouri, with features such as caves, springs, and sinkholes that form through the dissolution of limestone. Leith Creek is a small stream in Polk County, Missouri fed by two springs and the shallow unconfined Springfield Plateau aquifer, a highly karstified aquifer which is made up of limestone and minor interbedded shale-mudstone units. To determine if Leith Creek is gaining or losing, stream flow, water chemistry and temperature sensors were monitored. Stream flow results required multiple visits to take measurements while temperature sensors required two visits, one to install the dataloggers and another to remove the …

Comparison Of Machine Learning Models For Colon Cancer Survival: Predictive Modeling Approach, Reuben Adatorwovor, Motolani E. Ogunsanya, Bin Huang, Richard Charnigo, Olufunmilola Abraham

Biostatistics Faculty Publications

Background: Colon cancer is a leading cause of cancer-related deaths worldwide, with survival influenced by risk factors, treatment type, and patient characteristics. Traditional statistical models, such as Kaplan-Meier curves, have been widely used to estimate survival probabilities. However, these models often have difficulty handling complex interactions, covariates, and nonlinear relationships between risk factors. Recently, machine learning (ML) techniques have emerged as promising tools for improving survival prediction by handling large covariates and capturing complex patterns.

Objective: This study compares several ML models to accurately estimate colon cancer survival by leveraging data from the Kentucky Cancer Registry. By identifying key risk …

Disentangling The Inverse Relationship Between Cancer And Alzheimer’S Or Parkinson’S Disease: A Systematic Review On Mendelian Randomization Studies, Khine Zin Aung, Su Su Zin, Xian Wu, Zin W. Myint, Shama D. Karanth, Steven Estus, Christopher M. Norris, Peter T. Nelson, David W. Fardo, Erin L. Abner, Yuriko Katsumata

Biostatistics Faculty Publications

Introduction

Although studies have reported an inverse relationship between cancer and neurodegenerative diseases such as Alzheimer's disease (AD) and Parkinson's disease (PD), findings remain inconsistent. Observational studies are limited by survival bias and reverse causation. To better understand the relationship, we conducted a systematic review of Mendelian randomization (MR) studies examining both directions—assessing cancer as a risk factor for AD or PD, as well as AD or PD as exposures influencing cancer risk.

Methods

We systematically reviewed MR studies investigating the causal relation between cancer and either AD or PD. Cancer could be specified as either an exposure or an …

Incident Atherosclerotic Cardiovascular Disease Among Veterans By Gender Identity: A Cohort Study, Carl G. Streed Jr., Meredith S. Duncan, Kory R. Heier, T. Elizabeth Workman, Lauren B. Beach, Guneet K. Jasuja, Hill L. Wolfe, Landon D. Hughes, John R. O’Leary, Melissa Skanderson, Joseph L. Goulet

Biostatistics Faculty Publications

Background: Transgender and gender diverse (trans) populations are at elevated risk for atherosclerotic cardiovascular disease (ASCVD).

Objective: Measure the association of gender identity and gender-affirming hormone therapy (GAHT) with ASCVD outcomes.

Design: Cohort study.

Participants: Over 1 million veterans receiving care in the Veterans Health Administration.

Main Measures: Gender identity was identified via a validated natural language processing (NLP) algorithm. Incident ASCVD (acute myocardial infarction, ischemic stroke, or revascularization after the baseline date) was identified via International Classification of Diseases diagnosis codes among veterans without prevalent ASCVD. We calculated sample statistics stratified by gender identity and used Cox proportional hazard …

An 11-Year (2012-2022) Review Of Journal Of Athletic Training Publication Study Designs And Sample Sizes, Zachary K. Winkelmann, Samantha E. Scarneo-Miller, Emily C. Smith, Ryan M. Argetsinger, Lindsey E. Eberman

Rehabilitation Sciences Faculty Publications

Background

Research findings must be representative by creating a sample of individuals, ensuring the results can be generalized and applicable to a larger population, which has historically been guided by a power analysis. However, the varied research design methods require a unique approach to sampling and a formula for recruitment and size. Therefore, the purpose of this study was to analyze historical data from published manuscripts in the Journal of Athletic Training (JAT) relative to study design and sample sizes. A secondary purpose was to further explore metrics for survey-based research.

Methods

This descriptive analysis explored 1267 publications in each …

Adverse Childhood Experiences, Depression And Subjective Cognitive Decline By Gender: A Moderated Mediation Analysis, Monique J. Brown, Darlingtina K. Esiaka, Jaya Viswanathan

Behavioral Science Faculty Publications

Studies assessing depression as a mediating factor between adverse childhood experiences (ACEs) and subjective cognitive decline (SCD) are lacking. Therefore, the aims of this study were to: (1) determine the mediating role of depression in the association between ACEs and SCD; and (2) assess the moderating role of gender. Data were obtained from the 2023 Behavioral Risk Factor Surveillance Study (BRFSS) survey (N = 38,600). Crude and adjusted path analyses were used to determine the mediating role of depression between ACEs and SCD. Adjusted analyses controlled for sociodemographic confounders. ACEs were positively associated with depression (B = 0.129, p …

Model-Free Organization Of Patient Reported Outcomes Data: Geometrical Rep-Resentation Of The Modified Compartmen-Talization Method, Manasi Sheth, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

There is a recent advancement in the field of mathematics and statistics to understand the geometry or connectedness of the data due to the massive amounts of data being generated. The data provided for analyses are usually very large and need to be organized and minimized in order to make it more useful and meaningful. In biostatistics or medical field, it is important for patients to have access to high-quality, safe and effective and/ or efficacious medical products. It is quite necessary to ascertain that the patients and their care-partners stay at the center of the regulatory decision-making process. In …

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 …

Copula-Based Bayesian Model For Detecting Differential Gene Expression, Prasansha Liyanaarachchi, N. Rao Chaganty

Mathematics & Statistics Faculty Publications

Deoxyribonucleic acid, more commonly known as DNA, is a fundamental genetic material in all living organisms, containing thousands of genes, but only a subset exhibit differential expression and play a crucial role in diseases. Microarray technology has revolutionized the study of gene expression, with two primary types available for expression analysis: spotted cDNA arrays and oligonucleotide arrays. This research focuses on the statistical analysis of data from spotted cDNA microarrays. Numerous models have been developed to identify differentially expressed genes based on the red and green fluorescence intensities measured using these arrays. We propose a novel approach using a Gaussian …

Theory And Applications Surrounding Markov Chains, Joseph J. Quisito Jr., Gallean Brown, Elijah Yoder

Capstone Showcase

This capstone project explores the Markov Chain – a mathematical model used to describe systems that transition between states based on probabilities. It begins by introducing the fundamental concepts, including transition matrices, state classifications, and stationary distributions. The paper then applies Markov Chain theory to real-world scenarios, such as simulating Snakes and Ladders games, predicting soccer match outcomes for Manchester United, and generating texts from movie lines. Finally, it discusses key findings, challenges, and potential areas for future research in the field.

Analysis Of Sled Dog Biomechanics, Natalie Bender

Williams Honors College, Honors Research Projects

This paper is an analysis of data collected by Dr Rachel Olson and her team. The data was collected from the same set of sled dogs before and after training for the Iditarod race. The goal of this paper is to draw conclusions on whether the gait of sled dogs’ change with fitness level. The data was cleaned in R to find the average peak for forelimb joint angles per run for each dog. The data was analyzed with 3 different ANOVAs – one including both the shoulder and carpus, one for just the shoulder, and one for just the …

Time Series Modeling Of Akron Air Quality Index (Aqi) Data, Mason Yurich

Williams Honors College, Honors Research Projects

With the increase in population and industrialization around the world, climate has become a major concern for many researchers. One measure that has drawn much interest is air quality. There are available resources that track the Air Quality Index (AQI) in most large cities, but there is a general lack of information regarding Air Quality forecasts, even for one day in the future. This project aims to find a useful statistical model for representing and predicting the AQI measure in Akron, Ohio over time. By using historical air quality data from the United States Environmental Protection Agency and AQI.in, an …

Risk Factors For Autism Spectrum Disorder: A Statistical And Machine Learning Perspective, Supti Biswas

Open Access Master's Theses

his study explores factors associated with autism spectrum disorder (ASD) through analysis of a rich dataset comprising maternal and child characteristics. The data were carefully cleaned and preprocessed to address missing values and ensure analytical consistency. Statistical analyses and advanced visualization techniques were used to uncover patterns linking ASD status with variables such as maternal age, prenatal smoking, and education level. To further assess predictive capacity and feature importance, multiple supervised machine learning algorithms - logistic regression, decision trees, random forests, and support vector machines (SVM) - were applied. ASD appears to be influenced by a complex interplay of demographic …