Physical Sciences and Mathematics Commons^™

Statistical Methodologies For Count Time Series Analysis And Topological Data Analysis Of Medical Images, Yuhyeong Jang

Statistical Science Theses and Dissertations

This dissertation addresses two distinct topics related to count time series analysis and topological medical image analysis, respectively. The first part of the dissertation comprises an application of a count time series model to analysis of US monthly sex trafficking data and development of a new model for multivariate count data that exhibits serial dependence and overdispersion. By imposing a family of multivariate mixed Poisson distributions on the count random vector, the proposed model can accommodate a broad range of overdispersion as well as positive contemporaneous correlations. For maximum likelihood estimation, a computationally feasible EM-type algorithm is derived based on …

Exact X2 Statistic Critical Values For Dice Fairness Testing, Warren Campbell

SEAS Faculty Publications

Two questions were addressed: 1) What are the exact values of the c² statistic critical values? 2. Does a dice tower offer improved fairness of dice rolls?  Critical values of the statistic asymptotically approach those given by the continuous chi-square distribution, but the exact distribution is discrete.  The exact distributions only asymptotically approach the chi-square distribution, and the convergence is slow (1/number of rolls).  Th exact distributions are a function of the number of rolls, the chi-square distribution is not a function of the number of rolls.  Exact values of c² at the 90, 95, and 99 percent …

Ai For Regression Analysis And More, Eli Snir

Generative AI Teaching Activities

Students use Copilot and NotebookLM to create a dataset and develop statistical analyses including regression.

Statistical Methods In Research, Horahenage Dixon Vimalajeewa

UNL Faculty Course Portfolios

This course portfolio documents the design, delivery, assessment, student-learning evidence, and reflective evaluation of STAT 801A-700: Statistical Methods in Research, an online asynchronous service course designed to introduce students from diverse disciplinary backgrounds to foundational statistical reasoning and applied data analysis. The course is a non-calculus-based introduction to statistical methods used to answer research questions, with emphasis on collecting, organizing, describing, analyzing, and drawing conclusions from data. The course also emphasizes applications relevant to biology, agriculture, and other research-oriented fields. This is an online distance course aimed to develop students’ understanding of basic probability and statistical concepts, recognize the importance …

Multi-Level Variable Selection Using A Bart-Enhanced Mixed-Effects Framework, Keming Zhang, Yaoyao Li, Jungang Zou, Sijian Wang, Bernadette A. Fausto, Liangyuan Hu

College of Health Professions Faculty Papers

Selecting important individual- and cluster-level predictors has become increasingly critical in healthcare research, where data often exhibit hierarchical structures due to collection from multiple clusters. Mixed-effects models, which account for within-cluster correlation and between-cluster heterogeneity, are a natural approach for multilevel variable selection. However, currently available variable selection methods for multilevel data are predominantly based on mixed-effects models that impose restrictive parametric assumptions, potentially limiting their utility when the underlying relationships are nonlinear or involve interactions. While nonparametric methods have shown promise for variable selection in non-clustered data, they have been much less studied in the multilevel setting. Moreover, nonparametric …

An Analytical Framework For Quantifying Urban And Community Resilience To Natural Hazards From Cell-Phone Gps-Location And Traffic-Flow Data, Georgios Chatzikyriakidis

Civil and Environmental Engineering Theses and Dissertations

Urban areas are increasingly exposed to natural hazards while accommodating a growing share of the global population, yet a consistent science-based framework for quantifying urban and community resilience remains lacking. This dissertation develops a physics-based analytical framework grounded in statistical mechanics and the quantitative theory of Brownian motion. A city is conceptualized as a complex medium in which citizens move analogously to Brownian particles within a viscoelastic environment, influenced by socioeconomic interactions and infrastructure functionality.

A central premise is that urban resilience, interpreted as engineering resilience (an outcome), can be quantified through a single metric: the mean-square displacement MSD=⟨r²(t)⟩, of …

Bayesian Spatiotemporal Model For Counterfactual Estimation In Socioeconomic Studies, Duwani W. Gonzalez

Statistical Science Theses and Dissertations

Impact evaluations of regional development programs often require estimating counterfactual outcomes for a small number of treated regions using survey-based areal data. In practice, evaluators typically rely on two-group quasi-experimental methods such as propensity score matching (PSM) and Difference-in-Differences (DiD). These approaches perform poorly when only a few regions receive treatment, and when the set of observed covariates is limited or only partially relevant. Moreover, they typically do not explicitly exploit the spatial and temporal dependence present in survey-based areal data such as in ACS (American Community Survey). This dissertation develops a family of Bayesian spatial predictive models for directly …

A Statistical Analysis Of Current And Future Hurricane Activity In The North Indian Ocean, Basil Lund

2026 Symposium

A hurricane is defined as a tropical storm with winds sustained at 74 mph or greater. I examined major (category 3 and above) hurricane activity over the North Indian Ocean from the years 1972-2019 as reported by Colorado State University Hurricane Forecast Archive. Using RStudio, I conducted a binomial analysis of the CSU dataset to calculate probabilities of zero to ten years with one or more major North Indian Ocean hurricanes in the next decade. I conducted a geometric analysis to determine probabilities associated with waiting periods for the next year with a major hurricane, as well as a Poisson …

A Spatial Analysis Of Streetlights In The City Of Sugar Land, Samuel J. Trout

Data Science Undergraduate Honors Theses

The purpose of this paper is to analyze patterns between public safety and streetlighting for the City of Sugar Land, TX so that they may better protect their citizens.  The data involved come from the City of Sugar Land’s public works division and include type and location for all the attributes. The method of doing so involved visualizing the patterns of streetlights and their closest light readings to visualize which streetlights are underperforming using the Shiny package in R. Statistical tests were also used to quantify the association between lighting, crime occurrence, and crosswalks. From this, and the literature review, …

A Spatial Analysis Of Socioeconomic Characteristics And Community Vulnerability To Natural Hazards In Arkansas, Emma Steuber, Brad Peter

Geosciences Undergraduate Honors Theses

Natural hazards have become more frequent in recent years, and because of their destructive impacts, it is crucial to locate regions that are most affected historically by hazardous events and identify what socioeconomic characteristics co-occur spatially. While many studies have analyzed this relationship at national and state scales, few have focused specifically on Arkansas, and even fewer have incorporated multiple socioeconomic characteristics. Comparing the spatial distribution of community characteristics in Arkansas with zones of common natural hazards is essential for identifying who is most at risk. This study utilizes Geographic Information Science (GISci) to examine and map the spatial relationship …

Survival Patterns Among Adult And Pediatric Bone Cancer Patients, Ethan Estes

Mathematical Sciences Undergraduate Honors Theses

Recently noted, Huang et al. (2023), machine learning (ML) models, while offering great advantages over traditional statistical predictive modeling methods, are less explored in the analysis of survival and other similar time-to-event predictive data modeling. ML methods such as neural networks offer a great deal of promise but need to be further explored to investigate their comparative power in predicting survival outcomes. Focusing specifically on survival analysis in adult and pediatric bone cancer patients, traditional methods, like shown in Emmert-Streib and Dehmer (2019), will be shown with machine learning models using methods in Hothorn, Hornik, and Zeileis (2006). In this …

Enhancing Control Charting Schemes And Exploring New Assessment Metrics To Advance Quality Control And Cyber-Attack Detection In Manufacturing, Ahmad Al Majali

Dissertations

The increasing integration of digital technologies and industrial control systems in modern manufacturing has introduced new cybersecurity vulnerabilities within cyber–physical production environments. Malicious actors can exploit these vulnerabilities to induce subtle process deviations that degrade product quality while remaining undetected by conventional statistical monitoring tools. Such attacks can be deliberately engineered to manipulate process behavior through transient shifts that vary in magnitude, duration, and frequency. Despite extensive research on transient shifts caused by assignable causes in Statistical Process Control (SPC), limited attention has been given to intelligently designed cyber–physical attacks that exploit the structural characteristics and limitations of control charting …

Quantifying Noise In Monte Carlo Simulations Of Cosmic Rays In A Shock, Abhro Rahman

Theses and Dissertations

Cosmic rays are a phenomenon known to humankind from the early 1900s, and yet today the details of their physical behavior are not fully settled. While the observed spectral fluxes of cosmic rays follow a power-law trend when plotted against particle energy, the exact mechanisms under which they get to such high energies are still under study. This thesis will undertake a subsection of that task, where the Monte Carlo simulation of particle accelerations is described in Warren (2015), and we aim to identify and quantify the noise inherent in such simulations. This document takes a long path through particle …

Deep Learning Frameworks For Biological Data Integration And Generation, Alexa Beachum

Statistical Science Theses and Dissertations

Data integration represents a key area of research for analyzing the rapidly growing volume of high-dimensional biological data across sources, stages, and modalities. To model and understand these complex, often non-linear relationships, deep learning has become an increasingly powerful tool. Here, we present two novel deep learning frameworks that address distinct but complementary integration challenges. The first framework aligns single-cell omics data across temporal stages, and the second bridges imaging and omics modalities to generate patient-level molecular profiles.

In Chapter 1, we briefly summarize existing approaches---both statistical and deep learning-based---for single-cell omics data integration and discuss their limitations for handling …

Statistical Investigation Project, Ahmad Almomani Ph.D.

School of Arts & Sciences

The assignment was developed by SUNY Geneseo Professor Ahmad Almomani for the course, MATH 242: Elements of Probability and Statistics in the spring 2026 semester.

The objective for this assignment is that students will apply statistical methods to analyze real-world data and communicate meaningful conclusions.

Math 242: Elements Of Probability And Statistics (Syllabus), Ahmad Almomani Ph.D.

School of Arts & Sciences

This syllabus was developed by SUNY Geneseo Professor Ahmad Almomani during the spring 2026 semester.

Course Objectives:

Upon successful completion of Math 242, R/Elements of Probability and Statistics, a student will be able to: 

• Organize, present and interpret statistical data, both numerically and graphically; 
• Use various methods to compute the probabilities of events; 
• Analyze and interpret statistical data using appropriate probability distributions, e.g. binomial and normal. 
• Apply central limit theorem to describe inferences; 
• Construct and interpret confidence intervals to estimate means, standard deviations and proportions for populations; 
• Perform parameter testing techniques, including single and multi-sample tests for means, standard deviations …

Bayesball : A Comprehensive Framework For Predicting Ucl Injury, Brady M. Pinter, Will Best Ph.D.

SPARK Symposium Presentations

Ulnar Collateral Ligament (UCL) reconstruction, commonly referred to as Tommy John Surgery, has seen a significant rise among Major League Baseball (MLB) pitchers, prompting growing interest in identifying the mechanical and performance-based factors that contribute to injury risk. While previous studies have examined these relationships using traditional frequentist approaches separately, this study combines multiple different model techniques to present a broad framework for finding significant predictors of UCL Surgery. These models include Lasso and Ridge Regression,  Principal Component Regression (PCR) , Partial Least Squares Regression (PLS) , Random Forest, Multiple Linear Regression, and a Bayesian Statistical Model. Using these models, …

Statistics For Social Scientists Who Are A Little Afraid Of Them, Kristie L. Campana

MSU Authors Collection

This is a free textbook aimed at helping advanced undergraduate/beginner graduate students navigate statistics in the social sciences. All resources for this book are released under a creative commons license CC BY-SA 4.0. which means you are able to share, copy, and distribute the material in any medium or format, and that you can adapt and transform upon this content for any purpose. However, this can occur only under the following terms:

You must give appropriate attributions to the text, provide a link to the license, and indicate if any alterations to the text have been made. This can be …

Geoscience Mathematics Self-Efficacy Scale (Geomses) For Majors-Level Undergraduates: Psychometric Data, Michael Coe, Rory Mcfadden, Beth Pratt-Sitaula, Eric Baer

Science Education Resource Center (SERC)

Self-efficacy is often investigated as a key attitudinal component of academic persistence and performance, and self-efficacy surveys can serve in research models as efficient and non-threatening assessment tools to augment or substitute for achievement or performance measures. The Geoscience Mathematics Self-Efficacy Scale (GeoMSES) builds on prior work in measurement of self-efficacy for mathematics by focusing specifically on students’ capacity to apply mathematical skills to typical problems encountered in majors-level undergraduate geoscience courses or in professional geoscience settings. The scale was developed as part of program evaluation research for a set of learning modules designed to augment existing geoscience curricula. In …

Predicting Criminal Behavior In Major Us Cities, Madison A. Price

SPARK Symposium Presentations

In recent years, especially post pandemic, there has been a decrease in crime in the United States. Unfortunately, the country’s violent crime rates are still significantly higher compared to similar high-income countries, so what predicts crime in major American cities? There is tons of research to support the idea that demographics can offer some insight into predicting crime. There are countless online resources that seek to identify major crime centrals in the United States (Petrino, 2025). In the late 1990s, researchers noticed that crime rates in cities had a downward slope due to an important contributor: demographic change (Fox & …

Inferential Statistics For Industrial Organizational Psychologists: A Practical Guide For Testing Hypotheses Using R, Caitlin Lapine

Open Touro Created

2026

This text aims to provide a practical guide for students in industrial organizational psychology or related fields to complete inferential statistics using R open-source programming language. It provides information about when to use particular statistical analyses and how to perform those with statistical software.

Beyond The Lace Index: Benchmarking Machine Learning Architectures And Explaining 30-Day Hospital Readmission Risk With Shap Analysis, Carl E. Hughes Iii

Williams Honors College, Honors Research Projects

Unplanned 30-day hospital readmission remains a fundamental challenge in US healthcare, associated with increased risk to patient recovery and representing an estimated $52.4 billion in annual expenses (Beauvais et al., 2022). While the rigorously validated LACE index serves as the clinical standard for readmission modeling, its linear structure and four explanatory variables lack the complexity to capture the high-dimensional and interactive nature of patient risk. This study utilizes an admission granularity level cohort of the MIMIC-IV database to develop and compare machine learning architectures against the baseline LACE index. Due to the imbalanced prevalence of readmission, the penalized logistic regression, …

From Lap To Map: How Musical Scale, Place, And Play Drive The Interconnected Mario Kart World, Cameron Cummins

Honors Undergraduate Theses

With their deserts, castles, and ghost houses, the environments of Super Mario games are colorful, whimsical, and charming, but why are they so compelling, and what happens when our analysis of these environments extends beyond individual levels to expansive game worlds? Drawing on Cresswell’s theory of place (2014) and recent work on musical place-building in Mario Kart 8 (Heazlewood-Dale, 2024), I propose a spectrum between localized and globalized scale in games. As game environments become increasingly globalized, the music may be similarly altered to account for this shift in scale. Consequently, players may then encounter a broader, less musically congruent …

Non-Gaussian Phenomena In Light Scattering And Applications, Shubham Atul Dawda

Graduate Studies Theses and Dissertations 2026

Physical reality is rarely deterministic; which, when probed by electromagnetic fields that also fluctuate, leads to observables that are most efficiently modelled as statistical processes. At equilibrium, this is usually achieved by invoking the Gaussian statistics of underlying physical processes, however, in practice, one often encounters out-of-equilibrium conditions where Gaussian descriptions may not suffice. Such situations are plentiful in nature– from biology to astronomy. This dissertation addresses several non-Gaussian phenomena associated with light scattering, and includes specific models’ derivations, experimental techniques developments, and demonstrations of potential applications. The systematic presentation will consider circumstances that infringe upon specific assumptions of the …

Statistics 103a Instructor Guide, Elizabeth R. Wentworth

Open Educational Resources

This set of slides contains reading, original videos, activities and instructions for instructors to run a complete 12 week course in any modality. These resources can be used to supplement in-person instruction or can be used for either a hybrid or asynchronous course.

By The Numbers: Numeracy, Religion, And The Quantitative Transformation Of Early Modern England (Book Review), Calvin Jongsma

Faculty Work Comprehensive List

Reviewed Title: By the Numbers: Numeracy, Religion, and the Quantitative Transformation of Early Modern England by Jessica Marie Otis. New York, NY: Oxford University Press, 2024. 264 pp. ISBN: 9780197608777.

Smarter Disease Detection From Electronic Health Record Data: An End-To-End Ai-Augmented Pipeline For Computable Phenotyping, Dylan Owens

Statistical Science Theses and Dissertations

Electronic Health Records (EHR) contain a wealth of structured and unstructured patient data that can be leveraged for computable phenotyping, the process of algorithmically identifying patient cohorts with specific diseases or conditions. Traditional rule-based phenotyping approaches, while interpretable, often struggle with scalability, portability across institutions, and effective use of unstructured clinical narratives. Recent advances in large language models (LLMs) present new opportunities for synthesizing complex free-text information into concise, clinically meaningful representations. However, integrating LLMs into phenotyping workflows requires careful design to maintain transparency, interpretability, and measurable uncertainty—features essential for clinical adoption and downstream applications such as decision support.

We …

Statistical Methods For Joint Outcome Modeling And Dynamic Assessment Of Recurrent Events, Zifang Kong

Statistical Science Theses and Dissertations

Recurrent event data frequently arise in clinical studies where individuals experience repeated, possibly related, events over time. These data are often accompanied by sparse and irregular longitudinal measurements, creating challenges for traditional joint modeling approaches that struggle to account for time-dependent associations and within-subject correlations. We propose FRAILTY (Functional Regression with AutoRegressIve fraiLTY), a novel two-step framework that integrates functional principal component analysis (PACE) with a dynamic frailty model featuring autoregressive structure. FRAILTY accommodates both scalar and functional predictors and captures within-subject dependence across recurrent events. To further extend its utility, we develop a multivariate joint modeling framework that simultaneously …

Empirical Evaluation Of Bayes Error Rate Bounds In Binary Classification, Riley May

All Graduate Theses and Dissertations, Fall 2023 to Present

Classification tasks are fundamental in statistical machine learning. In classification tasks, a general goal is to build or select a model that can correctly classify data with as few errors as possible. However, for a particular dataset, the minimal number of errors achievable is seldom zero since overlap in the data makes errors unavoidable. As a result, it is often difficult for machine learning practitioners and data scientists to know whether classification errors can be reduced through further refinement. A potential solution to this lies in the Bayes error rate (BER). The BER is the lowest error rate achievable for …

Advancing Statistical Methods For Multivariate And Network Meta-Analysis, Yifei Wang

Statistical Science Theses and Dissertations

Multivariate meta-analysis (MMA) and network meta-analysis (NMA) are essential tools for synthesizing evidence across multiple correlated outcomes and treatments. However, these tools face practical challenges, including outcome reporting bias (ORB), unreported within-study correlations, and computational burden. ORB can distort effect estimates in MMA, while missing within-study correlations in multivariate NMA may lead to biased conclusions. To address these challenges, this dissertation introduces two novel statistical methods. For MMA, we propose SemiMMA, a semiparametric and scalable approach that treats ORB as a missing-not-at-random problem and combines inverse propensity weighting (IPW) with the generalized method of moments (GMM). For multivariate NMA, we …