Statistics and Probability Commons^™

Es/Ph/Sra 511: Applied Statistics, Minsoo Kang

GMAS Course Syllabi

No abstract provided.

Teacher Resilience: Validating An Instrument In The Us Context, Joy Mccreary

CGU Theses & Dissertations

The research surrounding teacher resilience is an emerging field, and the cultivation of instruments that measure teacher resilience should be bolstered. Specifically, there is a need for the creation of an instrument to be used in the United States. To address this need, I piloted the Multidimensional Teachers’ Resilience Scale (Mansfield & Wosnitza, 2015; Peixoto et al., 2020) in the US. The Multidimensional Teachers’ Resilience Scale (MTRS) comprises 29 items on four dimensions of resilience (professional, motivational, social, and emotional). The MTRS shows decent reliability and validity, but also demonstrates a need for a stronger instrument to be developed. As …

Evaluation Of Imputation Methods Focusing On Categorical Outcomes, Nadia Bernardo Mendoza

CGU Theses & Dissertations

In general, standard statistical analysis models typically rely on completely observed cases, excluding incomplete rows from the dataset. This approach poses particular challenges when the objective is to predict a rare outcome, especially when some of the ob servations with the rare outcome are incomplete. In such cases, the available information to support the model in predicting this event is reduces. Theoretically correct models may pre dict all instances in the majority class achieving high accuracy, but fail in predicting the rare cases, which are often the most interesting ones. Therefore, it is crucial to make the most of all …

Understanding Social Dynamics In Toxic Conversations And Public Health Intervention Acceptance On Social Media, Ana Aleksandric

Computer Science and Engineering Dissertations - Archive

Social media is now central to daily life, offering users a space to share content and opinions. However, these platforms also facilitate the spread of hate speech and misinformation, which can negatively impact public health. This dissertation develops methodologies to analyze social media data for insights that could inform health interventions. The research first examines user responses to toxic content, focusing on behavioral and emotional reactions, as well as group dynamics and bystander effects in toxic interactions. Another key focus is public opinion toward health interventions, particularly COVID-19 vaccination, using geolocated posts and analyzing factors such as race, ethnicity, and …

Advancing Deep Learning With Graph-Based Structural Insights: From Graph Classification To Semantic Segmentation, Xin Ma

Computer Science and Engineering Dissertations - Archive

Deep learning has profoundly transformed machine learning by offering sophisticated data representations, yet effectively incorporating structural information remains a challenge. Structural data, whether explicit or implicit, has the potential to significantly enhance the performance of deep learning tasks. This research investigates the benefits of structural information across three crucial tasks: classification, clustering, and segmentation. For explicit structural data, where inputs are directly represented as graphs, we investigate graph-level classification in brain connectivity networks. We introduce the Multi-resolution Edge Network (MENET), a novel framework designed to identify disease-specific connectomic benchmarks with high discriminatory power across diagnostic categories. MENET leverages graph-level representations …

Scalar-On-Function Regression: Estimation And Inference Under Complex Survey Designs, Ekaterina Smirnova, Erjia Cui, Lucia Tabacu, Andrew Leroux

Mathematics & Statistics Faculty Publications

Increasingly, large, nationally representative health and behavioral surveys conducted under a multistage stratified sampling scheme collect high dimensional data with correlation structured along some domain (eg, wearable sensor data measured continuously and correlated over time, imaging data with spatiotemporal correlation) with the goal of associating these data with health outcomes. Analysis of this sort requires novel methodologic work at the intersection of survey statistics and functional data analysis. Here, we address this crucial gap in the literature by proposing an estimation and inferential framework for generalizable scalar-on-function regression models for data collected under a complex survey design. We propose to: …

Assessing The Utility Of Breast Cancer Polygenic Risk Scores And Association With Clinical Factors In A Population Of Breast Cancer Patients, John L. Slunecka

Dissertations and Theses

INTRODUCTION: Breast cancer (BC) is the most common cancer among women and is classified as a complex disease. Advances in population genomics have led to the development of polygenic risk scores (PRSs) with the potential to enhance current risk models, but replication is often limited. OBJECTIVE: We sought to assess the predictive capabilities of two high-powered BC PRSs in a sample population selected for breast cancer. In addition, the capacity of the PRSs to predict clinical variables that could improve BC screening and treatments was explored. METHODS: Two published PRS algorithms (313 vs 3820) were used to score female subjects …

A 3-Step, Open-Data, Ride-Hailing Ridership Model With Pricing Applications, Richard A. Mucci

Theses and Dissertations--Civil Engineering

Researchers and practitioners studied the effects ride-hailing had in cities before the covid-19 pandemic. Previous research found ride-hailing to produce negative externalities, such as reducing transit ridership and increasing congestion in various cities. Since the pandemic, ride-hailing ridership has nearly recovered to pre-pandemic levels in Chicago. Ride-hailing ridership has grown steadily since the pandemic while a rider’s willingness to share their trip stagnated. Ride-hailing ridership nearly recovering to pre-covid levels in Chicago suggests that transportation planners, and policy makers, will need to continue assessing the impacts ride-hailing trips have in their cities.

Pickup and drop off locations in the Chicago …

Differential Impacts Of Weather Anomalies On Household Energy Expenditure Shares: A Comparison Of Clustered Panel Analysis Methods, Jordan Champion

Theses and Dissertations--Agricultural Economics

Recent emphasis on environmental justice has highlighted deficiencies in our energy system that produce disparities in accessibility and affordability for the most vulnerable. Meanwhile, the realities of a gradually warming climate and the onset of a global energy crisis (IEA 2022) have coincidently contributed to spikes in both energy prices and demand. These implications threaten to further exacerbate existing disparities for income-constrained and vulnerable populations, enhancing their risk of falling into prolonged insecurity. To ensure our transition to a just, sustainable future, we must first ensure equitable access to affordable and reliable energy for everyone. Combining household-level panel and state-level …

Outpatient Fall Prevention In Ambulatory Adults 65 Years Old And Over, Dorothy L. Osborne-White

Doctor of Nursing Practice (DNP) Scholarly Projects - Archive

Background: In the United States (U.S.), falls are the leading cause of injury among adults 65 and over, resulting in 36 million falls yearly (Moreland et al., 2020). According to the Centers for Disease Control and Prevention (CDC, 2023), one in four older adults experiences a fall each year. Falls are the world's second most prominent cause of accidental deaths (World Health Organization [WHO], 2021). Falls are the leading cause of both fatal and non-fatal injuries among older adults (Moreland et al., 2020).

Methods: A quality improvement project that included a fall bundle was implemented in a primary clinic. A …

Simulation Of Wave Propagation In Granular Particles Using A Discrete Element Model, Syed Tahmid Hussan

College of Graduate Studies: Theses & Dissertations

The understanding of Bender Element mechanism and utilization of Particle Flow Code (PFC) to simulate the seismic wave behavior is important to test the dynamic behavior of soil particles. Both discrete and finite element methods can be used to simulate wave behavior. However, Discrete Element Method (DEM) is mostly suitable, as the micro scaled soil particle cannot be fully considered as continuous specimen like a piece of rod or aluminum. Recently DEM has been widely used to study mechanical properties of soils at particle level considering the particles as balls. This study represents a comparative analysis of Voigt and Best …

Coral Scar Investigation: An Application Of Machine Learning And Computational Biology Methods To Understand Coral Holobiont Response To Various Tissue Loss Diseases, Emily W. Van Buren

Biology Dissertations - Archive

Coral disease is one of the biggest challenges facing coral reefs that actively changes biodiversity resulting in coral decline. With the rising threat of diseases, corals require biomarkers that reflect the immune systems and differences between common coral tissue loss diseases to best assist in coral restoration efforts. To obtain these biomarkers, my dissertation leverages two previously published datasets from two tissue loss disease exposure studies to investigate genes that are relevant for coral immune pathways, disease susceptibility, and classification between the diseases. In Chapter 2, I use comparative computational biology tools and protein assays to identify the melanin cascade …

ประสิทธิภาพของการทำนายความเสี่ยงการเกิดโรคเบาหวานร่วมกับความเสี่ยงการเกิดโรคความดันโลหิตสูงด้วยวิธีการเรียนรู้เชิงลึกสำหรับการจำแนกประเภทหลายเลเบล, วรรษา สุดใจ

Chulalongkorn University Theses and Dissertations (Chula ETD)

ในทางการแพทย์ การเรียนรู้เชิงลึกนิยมนำมาใช้ในการสร้างตัวแบบพยากรณ์ซึ่งค่อนข้างให้ผลที่ดีกว่าเมื่อเทียบกับตัวแบบดั้งเดิมแต่บางครั้งผู้ป่วยสามารถเป็นโรคพร้อมกันได้มากกว่าหนึ่งโรค การเรียนรู้เชิงลึกจึงถูกพัฒนาให้สามารถทำนายพร้อมกันได้หลายโรค เรียกว่าโครงข่ายประสาทเทียมประเภทจำแนกหลายเลเบล ถึงแม้โครงข่ายประสาทเทียมจะมีความสามารถที่ดีสำหรับการพยากรณ์แต่ยังมีความท้าทายในข้อมูลบางกลุ่มที่ข้อมูลมีจำกัด วิทยานิพนธ์ฉบับนี้จึงมีความสนใจที่จะทำการศึกษาโดยต้องการเพิ่มประสิทธิภาพของตัวแบบโดยรวมด้วยการใช้เลเบลผลลัพธ์ที่เกี่ยวข้องกันมาศึกษาผ่านข้อมูลโรคเบาหวานและโรคความดันโลหิตสูงซึ่งเป็นโรคที่มักเกิดร่วมกัน แบ่งการทดลองเป็นสองส่วนคือส่วนข้อมูลจำลองและข้อมูลจริงเพื่อเปรียบเทียบระหว่างโครงข่ายประสาทเทียมแบบป้อนไปข้างหน้าหลายเลเบลกับหนึ่งเลเบล ผลการศึกษาพบว่าในโครงข่ายประสาทเทียมหลายเลเบลให้ผลที่ดีในทางทฤษฎีที่ทดสอบกับข้อมูลจำลอง แต่ในข้อมูลจริงผลลัพธ์ของการใช้เลเบลที่มีความเกี่ยวข้องกันไม่สามารถลดค่าฟังชันการสูญเสียได้อย่างมีนัยสำคัญ แต่มีข้อดีคือช่วยลดความรุนแรงของปัญหา overfit ได้และสามารถให้ประสิทธิภาพการพยากรณ์ยังคงเทียบเท่าการใช้หนึ่งเลเบล

Developing Machine Learning And Time-Series Analysis Methods With Applications In Diverse Fields, Muhammed Aljifri

Theses and Dissertations

This dissertation introduces methodologies that combine machine learning models with time-series analysis to tackle data analysis challenges in varied fields. The first study enhances the traditional cumulative sum control charts with machine learning models to leverage their predictive power for better detection of process shifts, applying this advanced control chart to monitor hospital readmission rates. The second project develops multi-layer models for predicting chemical concentrations from ultraviolet-visible spectroscopy data, specifically addressing the challenge of analyzing chemicals with a wide range of concentrations. The third study presents a new method for detecting multiple changepoints in autocorrelated ordinal time series, using the …

Bayesian Analyses For Time-Varying Data And Opinion Dynamics, Torin Quinlivan

Graduate Research Theses & Dissertations

We consider Bayesian analyses for time-varying data from the opinion dynamics in social networks and the processes of sensorimotor learning. Firstly, understanding the underlying opinions of social media users is a difficult process, as they must be understood filtered through the messages sent. To understand how to best estimate the latent opinions, we use a Bayesian approach to estimate various characteristics of the users and the network. We present a model for using Bayesian methods for this problem, along with a simulation study to demonstrate effectiveness. Secondly, incentivization with punishments or rewards may affect human skill learning. To investigate the …

The Effects Of Bureaucratic Corruption On The Financial Constraints Of Nigerian Small Businesses, Obinna Franklin Ezeibekwe

Graduate Research Theses & Dissertations

In this paper, I conduct the first empirical analysis to examine the impact of bureaucratic corruption on small business financial constraints in Nigeria. This study is also the first to compare the average treatment effect (ATE) with the local average treatment effect (LATE) framework to account for potential variations in treatment effects for small Nigerian firms with less than 100 employees. Using the bivariate probit method and two binary instruments, I find that corruption significantly increases the likelihood of financial constraints for a typical micro, small, and medium enterprise (MSME) by approximately 64 to 68 percentage points. When I use …

Principal Component Analysis With Application To Credit Card Data, Elenor Cain

Schultz-Werth Award Papers

Principal Component Analysis (PCA) is a type of dimension reduction technique used in data analysis to process the data before making a model. In general, dimension reduction allows analysts to make conclusions about large data sets by reducing the number of variables while retaining as much information as possible. Using the numerical variables from a data set, PCA aims to compute a smaller set of uncorrelated variables, called principal components, that account for a majority of the variability from the data. The purpose of this paper is to understand PCA and determine which principal components should be kept from a …

Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe

Data Science and Data Mining

Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …

Predicting Superconducting Critical Temperature Using Regression Analysis, Roland Fiagbe

Data Science and Data Mining

This project estimates a regression model to predict the superconducting critical temperature based on variables extracted from the superconductor’s chemical formula. The regression model along with the stepwise variable selection gives a reasonable and good predictive model with a lower prediction error (MSE). Variables extracted based on atomic radius, valence, atomic mass and thermal conductivity appeared to have the most contribution to the predictive model.

Cryptographic Algorithms, Cryptocurrencies, And A Predictive Model Of Bitcoin Value By Pls Regression, Paul Kenneth O'Connor

Masters Theses

"With the invention of Bitcoin in 2009, as a seemingly timed response to the ongoing financial crisis, the popularity of the cryptocurrency has since continued to grow. Just this year, the Security Exchange Commission approved Bitcoin for exchange traded funds, allowing major investment firms to begin product trading. With this approval, and during this very moment of writing, Bitcoin has entered a bull market and reached a record value of over 72,000 USD. In addition, the Bitcoin halving event in April of 2024 is expected to increase demand even further. It has been anticipated that Bitcoin and other cryptocurrencies will …