Statistics and Probability Commons^™

Measuring The Misplacement Of Data From Multidimensional Scaling, Lucy Liu

Holster Scholar Projects

Multidimensional scaling (MDS), in which high-dimensional data is projected to a lower dimensional map, is often followed by clustering in the reduced plot. To examine the effect of MDS on clustering, we simulate several data structures and apply clustering methods, including topological data analysis. We first perform clustering using the data in the original, high-dimensional space, then perform MDS to scale the data down to a lower dimension, cluster on this scaled data, and compare differences in the results. We found that MDS can often decrease clustering performance, and is unable to correctly represent data structures with unique shapes or …

Mathematics In Contemporary Society, Patrick J. Wallach

Open Educational Resources

Mathematics in Contemporary Society is the textbook that corresponds to MA-321, the course of the same name. The course is designed to provide students with mathematical ideas and methods found in the social sciences, the arts, and in business. Topics will include fundamentals of statistics, scatterplots, graphics in the media, problem solving strategies, dimensional analysis, mathematics in music and art, and mathematical modeling. EXCEL is used to explore real world applications.

Rates Of Reporting And Analyzing Race And Ethnicity In Athlete-Specific Sports Medicine Research: A Systematic Review, John H. Sonnier, Carlo Coladonato, Irfan A. Khan, Gregory Connors, Ryan W. Paul, Anya T. Hall, Emma E. Johnson, Meghan E. Bishop, Fotios P. Tjoumakaris, Kevin B. Freedman

Rothman Institute Papers

BACKGROUND: Race- and ethnicity-based differences in treatment access and outcomes have been reported in the orthopaedic sports medicine literature. However, the rate at which race and ethnicity are reported and incorporated into the statistical analysis of sports medicine studies remains unclear.

PURPOSE: To determine the rate at which race and ethnicity are reported and analyzed in athlete-specific sports medicine literature.

STUDY DESIGN: Systematic review; Level of evidence, 4.

METHODS: Using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, articles published by the 3 journals of the American Orthopaedic Society for Sports Medicine between 2017 and 2021 were considered …

Assessing The Impact Of Femur Morphological Variations On Pediatric Hip Joint Biomechanics Using Statistical Shape Modeling, Tamara Chambers

Doctoral Dissertations and Master's Theses

This dissertation aimed to (1) quantify morphological variations in the pediatric hip joint and (2) evaluate the sensitivity of an infant musculoskeletal model (MSM) to these variations, considering hip joint center estimation errors. A shape statistical model (SSM) of decedent infant femurs from the Ortolani collection was created using ShapeWorks, capturing key morphological features, such as variations in the femoral neck-shaft and anteversion angles. Seven synthetic femurs were generated from the SSM to create SSM-informed MSMs, which were systematically evaluated through kinematics and kinetics analyses in OpenSim. Incorporating the SSM led to slight changes in the pediatric MSMs’ kinematics but …

Communities That Heal Intervention And Mortality Including Polysubstance Overdose Deaths: A Randomized Clinical Trial, Bridget Freisthler, Rouba A. Chahine, Jennifer Villani, Redonna Chandler, Daniel J. Feaster, Svetla Slavova, Jolene Defiore-Hyrmer, Alexander Y. Walley, Sarah Kosakowski, Arnie Aldridge, Carolina Barbosa, Sabana Bhatta, Candace Brancato, Carly Bridden, Mia Christopher, Tom Clarke, James David, Lauren D'Costa, Irene Ewing, Soledad Fernandez, Erin Gibson, Louisa Gilbert, Megan E. Hall, Sarah Hargrove, Timothy Hunt, Elizabeth N. Kinnard, Lauren Larochelle, Aaron Macoubray, Shawn R. Nigam, Edward V. Nunes, Carrie B. Oser, Sharon Pagnano, Peter J. Rock, Pamela Salsberry, Aimee Shadwick, Thomas J. Stopka, Sylvia Tan, Jessica L. Taylor, Philip M. Westgate, Elwin Wu, Gary A. Zarkin, Sharon L. Walsh, Nabila El-Bassel, T. John Winhusen, Jeffrey H. Samet, Emmanuel A. Oga

Biostatistics Faculty Publications

IMPORTANCE: The HEALing Communities Study (HCS) evaluated the effectiveness of the Communities That HEAL (CTH) intervention in preventing fatal overdoses amidst the US opioid epidemic.

OBJECTIVE: To evaluate the impact of the CTH intervention on total drug overdose deaths and overdose deaths involving combinations of opioids with psychostimulants or benzodiazepines.

DESIGN, SETTING, AND PARTICIPANTS: This randomized clinical trial was a parallel-arm, multisite, community-randomized, open, and waitlisted controlled comparison trial of communities in 4 US states between 2020 and 2023. Eligible communities were those reporting high opioid overdose fatality rates in Kentucky, Massachusetts, New York, and Ohio. Covariate constrained randomization stratified …

Making Plans Findable, Accessible, Interoperable, And Reusable With Data Infrastructure: A Search Engine For Constructing, Analyzing, And Visualizing Planning Documents, Lindsay Poirier, Dexter Antonio, Makenna Dettmann, Tiffany Eng, Jennifer Ganata, Sujoy Ghosh, Mirthala Lopez, Ranesh Karma, Asiya Natekal, Catherine Brinkley

Statistical and Data Sciences: Faculty Publications

Local land-use plans help guide future development, but it is often difficult to compare content across jurisdictions, making regional coordination and plan evaluation challenging. This research reviews federal, state, and local data infrastructure guidance for land-use plans and compares such guidance to compliance with a California use-case. Findings indicate a number of obstacles to fostering data sharing and comparative analysis of plans: there is currently no central repository of land-use plans; plans are not uniform in format and are often out of date; many plans are not machine-readable thereby inhibiting text extraction, and planning language varies so greatly that there …

A Qualitative Study Exploring Graduated Medical Residents’ Research Experiences, Barriers To Publication And Strategies To Improve Publication Rates From Medical Residents, Dorothy Kamya, Brigette Macharia, Wangari Siika, Caroline Mbuba

Anaesthesiology, East Africa

Background: In Kenya, postgraduate medical residents must complete a research dissertation for their Master of Medicine studies. However, the subsequent publication rate is lower than in higher-income settings, limiting the availability of population-specific data. This study explored residents’ experiences with research, reasons for the low publication rate, and strategies to improve publication rates.

Methods: In-depth interviews were conducted with 9 faculty members and non-academic support staff, as well as 18 Master of Medicine graduates who had successfully completed their research projects, to investigate their experiences with conducting, supervising, and publishing research. The interview data was analysed using inductive …

Bayesian Nonparametric Models For Pooled Data, Yizeng Li

Theses and Dissertations

This work examines two applications of pooling: group testing and pooled biomonitoring. Group testing, introduced by Dorfman in the early 1940s, was initially developed to screen for syphilis among U.S. inductees during World War II. Since then, the approach has demonstrated cost-saving benefits in diverse fields, including drug discovery, genetics, and infectious disease testing. While various regression methods—parametric, nonparametric, and semiparametric—have been proposed to analyze group testing data, they fall short in addressing age-related disparities in disease presence if such variations exist. In Chapter 2, we address this gap by expanding varying coefficient regression within a Bayesian framework to accommodate …

Reframing And Advancing Academic Mentorship To Support New And Early Career Faculty Members, Juanita-Dawne R. Bacsu, Patricia C. Heyn, Steffi Kim, Zahra Rahemi, Monique J. Brown, Darina V. Petrovsky, Justine S. Sefcik, Jodi L. Southerland, Jeremy Holloway, Elyse Couch, Ayse Malatyali, Matthew L. Smith

Faculty Publications

Mentorship is critical to fostering professional growth and career development in academia. However, academic mentorship is often an informal activity that is overlooked and under researched. There is much ambiguity and uncertainty surrounding mentorship roles and strategies in academia. This paper provides recommendations and strategies to reframe and advance academic mentorship to support new and early career faculty.

Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth Ph.D., James Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich

Faculty Publications

Merriam-Webster and Oxford define a xenobiotic as any substance foreign to living systems. Allura Red AC (a.k.a., E129; FD&C Red No. 40), a synthetic food dye extensively used in manufacturing ultra-processed foods and therefore highly prevalent in our food supply, falls under this category. The surge in synthetic food dye consumption during the 70s and 80s was followed by an epidemic of metabolic diseases and the emergence of early-onset colorectal cancer in the 1990s. This temporal association raises significant concerns, particularly given the widespread inclusion of synthetic food dyes in ultra-processed products, notably those marketed toward children. Given its interactions …

Allura Red Ac Is A Xenobiotic. Is It Also A Carcinogen?, Lorne J. Hofseth, James R. Hébert Scd, Elizabeth Angela Murphy, Erica Trauner, Athul Vikas, Quinn Harris, Alexander A. Chumanevich

Faculty Publications

Merriam-Webster and Oxford define a xenobiotic as any substance foreign to living systems. Allura Red AC (a.k.a., E129; FD&C Red No. 40), a synthetic food dye extensively used in manufacturing ultra-processed foods and therefore highly prevalent in our food supply, falls under this category. The surge in synthetic food dye consumption during the 70s and 80s was followed by an epidemic of metabolic diseases and the emergence of early-onset colorectal cancer in the 1990s. This temporal association raises significant concerns, particularly given the widespread inclusion of synthetic food dyes in ultra-processed products, notably those marketed toward children. Given its interactions …

Burden Of Disease Attributable To High Body Mass Index: An Analysis Of Data From The Global Burden Of Disease Study 2021, Xiao-Dong Zhou, Qin-Fen Chen, Wah Yang, Mauricio Zuluaga, Giovanni Targher, Christopher Byrne, Luca Valentu, Fei Luo, Christos Katsouras, Christopher Opio

Internal Medicine, East Africa

Background: Obesity represents a major global health challenge with important clinical implications. Despite its recognized importance, the global disease burden attributable to high body mass index (BMI) remains less well understood.

Methods: We systematically analyzed global deaths and disability-adjusted life years (DALYs) attributable to high BMI using the methodology and analytical approaches of the Global Burden of Disease Study (GBD) 2021. High BMI was defined as a BMI over 25 kg/m2 for individuals aged ≥20 years. The Socio-Demographic Index (SDI) was used as a composite measure to assess the level of socio-economic development across different regions. Subgroup analyses considered age, …

Innovation Challenges In The Air Force Sbir Program: From The Small Businesses' Perspective, Hart J. Holt, Amy M. Cox, Scott Drylie, David S. Long, Alfred E. Thal Jr., Robert D. Fass

Faculty Publications

Every year the United States invests $3.2 billion in the Small Business Innovation Research (SBIR) program to promote innovation among the nation’s small businesses. Half of this investment is from the DoD. This research considers the challenges faced by small businesses innovating with the DoD, particularly those awarded SBIR contracts with the United States Air Force. The authors surveyed 286 unique small businesses that were previously awarded an Air Force SBIR contract. By asking the survey respondents open-ended questions and categorizing their responses, they pinpoint unaddressed challenges from the small business perspective. By categorizing survey responses through Qualitative Content Analysis, …

Genomic And Socioeconomic Determinants Of Racial Disparities In Breast Cancer Survival: Insights From The All Of Us Program, Nubaira Rizvi, Hui Lyu, Leah Vaidya, Xiao Cheng Wu, Lucio Miele, Qingzhao Yu

School of Public Health Faculty Publications

Background: Breast cancer outcomes are worse among Black women in the U.S. compared to White women. While extensive research has focused on risk factors contributing to breast cancer; the role of genomic elements in health disparities between these racial groups remains unclear. This study aims to identify genomic variants and socioeconomic status (SES) determinants influencing racial disparities in breast cancer survival through multiple mediation analyses. Methods: Our investigation is based on the NIH-supported All of Us (AoU) program and analyzes 7452 female participants with malignant tumors of breast, including 5073 with genomic data. A log-rank test reveals significant racial differences …

Development Trends Of Large Models And Tencent’S Independent Innovation Practice, Jason Si

Bulletin of Chinese Academy of Sciences (Chinese Version)

The article discusses the emerging trends and application prospects of current large models, using Tencent’s Hunyuan large model as an example. It focuses mainly on innovations and implementations of large models in China. Companies like Google, Meta, and OpenAI have launched powerful models such as Google’s Gemini and Meta’s Llama 3, which have made significant progress in multi-modal applications and reasoning capabilities. China’s large models have significantly improved performance and efficiency by adopting the MoE (Mixture of Experts) architecture. Specifically, with its self-developed MoE trillion-parameter large model and deep learning framework, Tencent has made breakthrough advancements in large model technology …

Human Capital At Home: Evidence From A Randomized Evaluation In The Philippines, Noam Angrist, Sarah Kabay, Dean S. Karlan, Lincoln Lau, Kevin M. Wong

Education Division Scholarship

Children spend most of their time at home in their early years, yet efforts to promote human capital at home in many low- and middle-income settings remain limited. We conduct a randomized controlled trial to evaluate an intervention which encourages parents and caregivers to foster human capital accumulation among their children between ages 3 and 5, with a focus on math and phonics skills. Children gain 0.52 and 0.51 standard deviations relative to the control group on math and phonics tests, respectively (p<0.001). A year later effects persist, but math gains dissipate to 0.15 (p=0.06) and phonics to 0.13 (p=0.12). Effects appear to be mediated largely through instructional support by parents and not other parent investment mechanisms, such as more positive parent-child interactions or additional time spent on education at home beyond the intervention. Our results show that parents can be effective conduits of educational instruction even in low-resource settings.

Improvement And Evaluation Of Multiple Imputation By Heckman's One-Step Mle For Binary Mnar Outcomes And Various Types Of Mar Covariates, Xin W. Shore

Mathematics & Statistics ETDs

Missing data is inevitable in clinical epidemiology. It becomes one of the major challenges in the analyses and can potentially undermine the validity of results and conclusions. Although methods for handling missing data with mechanisms of missing completely at random (MCAR) or missing at random (MAR) have been widely researched, methods adapted for the missing not at random (MNAR) mechanism are less studied. Galimard et al. (2018) have derived a method to use multiple imputation by Heckman's One-Step ML Estimation for binary MNAR outcome and continuous MAR covariates (MIHEml). This dissertation focuses on updating MIHEml in terms of …

Assessing The Adequacy Of A Prediction Model, Abhaya Indrayan, Sakshi Mishra Ms

COBRA Preprint Series

No abstract provided.

Effect Of Correlation Boundaries On Collinearity And Bias In Ordinary Least Square Regression, Yumo Xue

All ETDs from UAB

Linear regression (LM) stands as one of the prevailing methods used to assess the association between a dependent variable and independent variables, typically estimated using ordinary least squares. However, this approach frequently faces obstacles when applied to biomedical data, due to the issue of correlated independent variables (collinearity). Collinearity is a potential issue in observational studies, where researchers might omit less significant correlated variables from their model. This is often guided by the rule of thumb that a Variance Inflation Factor (VIF) of 10 indicates severe collinearity. Our hypothesis is that relying on this rule of thumb is arbitrary, and …

Supplementary Files For: Quantifying The Impact Of Rain-On-Snow Induced Flooding In The Western United States, Emma Watts, Brennan Bean

Browse all Datasets

Serious flooding can happen when rain falls on snow, which we call a rain-on-snow (ROS) event. Increasing our understanding of the behavior of floods resulting from ROS events can help us design better systems to manage flood water and prevent it from causing damage. This thesis explores how ROS events affect streamflow in the Western United States by examining the weather conditions that precede a streamflow surge. We classify stream surges as ROS or non-ROS induced based on these weather conditions, which helps us separate floods caused by ROS events from those caused by other factors. By comparing these different …

Review Of Fluke: Chance, Chaos, And Why Everything We Do Matters, Walter Hill

The Journal of Social Encounters

No abstract provided.

Pooling And Winsorizing Machine Learning Forecasts To Predict Stock Returns With High-Dimensional Data, Erik Mekelburg, Jack Strauss

Finance: Faculty Scholarship

We evaluate US market return predictability using a novel data set of several hundred ag- gregated firm-level characteristics. We apply LASSO, Elastic Net, Random Forest, Neural Net, Extreme Gradient Boosting, and Light Gradient Boosting Machine methods and find these models experience large prediction errors that lead to forecast failures. However, winsorizing and pooling machine learning model forecasts provides consistent out-of-sample predictability. To assess robustness, we apply machine learning methods to high-dimensional data for Canada, China, Germany and the UK as well as the Goyal-Welch data. All machine learning models we consider, except for the ensemble pooled methods, fail to significantly …

Generalized Periodicity And Applications To Logistic Growth, Martin Bohner, Jaqueline Mesquita, Sabrina Streipert

Mathematics and Statistics Faculty Research & Creative Works

Classically, a continuous function f:R→R is periodic if there exists an ω>0 such that f(t+ω)=f(t) for all t∈R. The extension of this precise definition to functions f:Z→R is straightforward. However, in the so-called quantum case, where f:qN0→R (q>1), or more general isolated time scales, a different definition of periodicity is needed. A recently introduced definition of periodicity for such general isolated time scales, including the quantum calculus, not only addressed this gap but also inspired this work. We now return to the continuous case and present the concept of ν-periodicity that connects these different formulations of periodicity for …

The Cubic-Quintic Nonlinear Schrödinger Equation With Inverse-Square Potential, Alex H. Ardila, Jason Murphy

Mathematics and Statistics Faculty Research & Creative Works

We consider the nonlinear Schrödinger equation in three space dimensions with a focusing cubic nonlinearity and defocusing quintic nonlinearity and in the presence of an external inverse-square potential. We establish scattering in the region of the mass-energy plane where the virial functional is guaranteed to be positive. Our result parallels the scattering result of [11] in the setting of the standard cubic-quintic NLS.

Analysis Of Data Containing Outliers, David L. Farnsworth

Articles

A strategy for accommodating outlying observations, as well as non-representative, suspect, missing, or otherwise troubling observations, is described. Each unusual observation is decomposed into the sum of two components. One component is the value implied by the trusted observations in the data set. The other component is the unusual part. In this way, the fitting of the data set can then proceed, and, additionally, a numerical value can be ascribed to the unusual part. The method offers not only an antidote for observations with irregular numerical values, which often have the power to contaminate and alter analyses, but also a …

Limit Theorems For L-Functions In Analytic Number Theory, Asher Roberts

Dissertations, Theses, and Capstone Projects

We use the method of Radziwill and Soundararajan to prove Selberg’s central limit theorem for the real part of the logarithm of the Riemann zeta function on the critical line in the multivariate case. This gives an alternate proof of a result of Bourgade. An upshot of the method is to determine a rate of convergence in the sense of the Dudley distance. This is the same rate Selberg claims using the Kolmogorov distance. We also achieve the same rate of convergence in the case of Dirichlet L-functions. Assuming the Riemann hypothesis, we improve the rate of convergence by using …

Multi-Label Classification Using Conformal Prediction, Chhavi Tyagi

Dissertations

In many machine learning applications, such as image tagging, document classi-fication, and medical diagnosis, a data instance can be associated with multiple classes in parallel so that each instance is associated with multiple response variables simultaneously defining multi-label classification. Standard multi-label classification methods that provide point predictions have been developed. They lack in quantifying the uncertainty of predictions. These methods also lack in accounting for label dependencies and are very computationally expensive. This dissertation develops two methods of multi-label classification using conformal prediction that quantify the uncertainty of predictions. Chapter 1 introduces notations and tools that have been used in …

Large Deviation Theory In Stochastic Processes: Applications To Biological Modeling, Moshe C. Silverstein

Dissertations

This dissertation delves into developing and applying stochastic models to analyze complex biological systems. It leverages Large Deviation Theory (LDT) to gain insights into these systems, focusing on two key examples: neural networks and calcium signaling dynamics. Traditional deterministic methods frequently fail to capture biological processes' randomness and inherent variability. Meanwhile, many stochastic approaches struggle to be mathematically tractable or provide accessible insights. The approach introduced in this study provides rigorous mathematical frameworks to enhance understanding of these stochastic behaviors while remaining tractable and insightful.

A stochastic model for a random biological neural network is constructed that addresses the dependencies …

Investigating Mixed Effects Random Forest Models In Predicting Satisfaction With Online Learning In Higher Education, Jiaqi (Jackie) Shi

Electronic Theses and Dissertations

One of the many impacts of the COVID-19 pandemic has been the increasing prevalence and accessibility of online education. This trend has also introduced challenges for students, instructors, and institutions. This study examines factors affecting online course satisfaction, focusing on individual, instructor, and institutional level characteristics with clustered, separated train and test datasets across two terms. This study compares Hierarchical Linear Model (HLM), Non-clustered and Clustered Random Forest (RF & MERF) models to understand these impacts. This intention is to provide a comprehensive framework comparing traditional HLM with the latest developed MERF models while delving into the effectiveness of RF …

The Naked Truth Of My Voice: Surveying The Soul Of An Aspiring Educator From Dusk To Dawn: An Arts-Based Auto-Criticism Inquiry, Siddharth Maan

Electronic Theses and Dissertations

This dissertation is based on auto-criticism, a new qualitative inquiry that combines educational criticism and arts-based research. It focuses on my own experiences as an aspiring educator who struggles with stuttering and public speaking anxiety. The study aims to describe, interpret, evaluate, and thematize my experiences of dealing with public speaking anxiety and stuttering before, during, and after classroom teaching. In addition, this dissertation highlights the potential contributions of auto-criticism as a methodology to qualitative research and higher education. I utilized journaling to document data. Photos and music were also used to enhance the understanding of my lived experiences and …