Statistics and Probability Commons^™

Bridging The Gap Between Career Expectations Versus Labor Market Realities, Reinette P. Madrid, Grethel T. Ledesma, Ignatius Aryono Putranto, Jyro B. Triviño

Leadership and Strategy Faculty Publications

Most students lack awareness regarding the labor market outcomes for their chosen college majors. This study aims to answer what factors affect the career expectations of graduating students at Quezon City University and how these expectations align with the prevailing labor market situation. It employed descriptive, causal, and explanatory research using a sample of 108 respondents from fourth-year information technology students for the school year 2021 to 2022. Eight of the nine null hypotheses were rejected by employing multinomial logistic and linear regression. Student fixed effects and other labor market outcomes significantly predicted salary, estimated stability, and estimated skills in …

Likelihood-Based Inference For The Poisson–Tweedie Family, Si Chen

Theses and Dissertations (Comprehensive)

This thesis develops likelihood-based methodology for flexible modeling and inference for heterogeneous count data within and adjacent to the Poisson–Tweedie family. A mean–dispersion regression framework is introduced to jointly relate the mean and dispersion to covariates, enabling separation of covariate-driven heterogeneity from distributional parameters. Likelihood inference is implemented using recursive probability mass function evaluation, and performance is assessed through simulation and applications including RNA-sequence counts. The framework is extended to accommodate excess zeros via zero-inflated Poisson–Tweedie models, with full likelihood-based estimation and improved risk estimation illustrated using insurance claim data. Finally, boundary behaviour is investigated through the discrete stable distribution …

Time Series Mediation Analysis With Non-Linear And Machine Learning Methods, Timothy Marsh

Theses and Dissertations (Comprehensive)

Non-linearity in mediation analysis has been primarily studied in the context of binary variables which encode a treatment and control, estimating a `direct` and `indirect` effect of a covariate (a.k.a. treatment) X on a response Y, with a third variable M (the `mediator`) that is affected by X and in turn affects Y. The focus of mediation analysis in general is to quantify the overall effect of X on Y, including the effect through M. This presentation will focus on one or more continuous treatments and apply non-linear methods, including splines and machine learning models, to mediation analysis in a …

Robust Statistical Methods For Microbiome Abundance Data, Yiming Shi

WUSM Theses and Dissertations – All Programs

Differential abundance analysis in microbiome studies aims to identify taxa whose abundance differs across biological or clinical conditions. The observed data are typically taxon-specific sequencing read counts, representing reads assigned to different taxa within each sample. These counts are indirect measurements of the underlying microbial abundance profile and are constrained by sample-specific library sizes. Microbiome count data are also typically sparse, overdispersed, and heteroscedastic. Together, these characteristics create substantial challenges for differential abundance analysis and make the results highly sensitive to normalization procedures, model specification, and the statistical methods used for inference.

Normalization defines the scale on which samples are …

Revisiting Ulam Stability For Boundary Value Problems, Martin Bohner, Snezhana Hristova, Agnieszka B. Malinowska, Ewa Girejko

Mathematics and Statistics Faculty Research & Creative Works

The main goal of this paper is to apply Ulam stability theory to boundary value problems for dynamic equations, while addressing several common misconceptions found in the existing literature. We identify the key issues that arise when applying Ulam stability to such problems and propose three distinct approaches to overcome them. To enhance clarity and accessibility, we begin with nonlinear ordinary differential equations and subsequently extend the analysis to nonlinear dynamic equations on time scales. Since a time scale is defined as any nonempty closed subset of the real numbers, our results are applicable to dynamic equations on continuous, discrete, …

The Paradox Of Sport: Adolescent Athletes Are Highly Sedentary Across Competition, Practice, And Rest Days, Jena Heck Street, Abigail M. Larson, Roman De Guia, Naveen K. Bansal, Sandra K. Hunter, Jacob J. Capin

Mathematical and Statistical Science Faculty Research and Publications

Background

Elite athletes are highly sedentary outside of sports and must stay active after retiring to remain healthy. While sports are often recommended to improve physical activity, the effect of sport training day on daily physical activity levels in adolescent athletes is not well described. The purpose was to assess (1) physical activity (i.e., step counts and activity score [METs]) and (2) sedentary behavior (e.g., total duration and prolonged [> 1 h] bouts) in male and female adolescent athletes across different training days (i.e., competition, practice, and rest days).

Methods

Adolescent basketball and volleyball athletes wore an accelerometer continuously for …

Visualizing The Regression Lines And The Correlation Coefficient For Bivariate Discrete Random Variables Authors, Mamunur Rashid, Jyotirmoy Sarkar

Mathematics Faculty Publications

Sarkar and Rashid (2024) visualized the joint probabilities of two discrete random variables using a 2D disc plot from which they graphically obtained the conditional distributions of 𝑌 given 𝑋=𝑎, and 𝑋 given 𝑌=𝑏; and the marginal distributions of 𝑋, 𝑌, 𝑋+𝑌, 𝑋−𝑌, and 𝑐𝑋+𝑑𝑌. Sarkar and Rashid (2025) visualized the mean, mean deviation, and standard deviation of any discrete random variable using a disc plot. Here, starting with the 2D disc plot, we visualize the correlation coefficient between X and Y, and the linear regression of one variable from the other. Such visualizations help users better comprehend these concepts. …

A Pick-The-Winner Design Framework For Two-Arm Randomized Clinical Trials, Wanni Lei, Vernon M. Chinchilli, Xi Kathy Zhou

COBRA Preprint Series

We propose a general pick-the-winner design framework for two-arm randomized clinical trials with binary and continuous endpoints that meets prespecified type I error rate and power requirements for winner selection. Traditional two-group comparison tests often require sample sizes that are impractical in early phase trials. Existing pick-the-winner designs offer an efficient alternative but generally emphasize the probability of correct selection rather than explicit control of type I and II error rates for winner selection. Our framework comprises four components: pick-the-winner-based hypotheses, winner selection strategies, definitions of type I error rate and power for winner selection, and design calibration. It accommodates …

Statistical Modeling Of Solar Flares During Solar Cycle 24 (2010–2015), Walae Dahmany '28

Annual Student Research Poster Session

Solar flares are energetic events driven by the rapid release of magnetic energy in the solar atmosphere and are an important source of space-weather disturbances. This study examined six years of solar flare activity during Solar Cycle 24 (2010–2015) using GOES X-ray observations and NOAA solar activity data. Flare frequency and intensity were analyzed alongside sunspot number, F10.7 cm radio flux, solar-cycle phase, Hale magnetic classification, previous flare activity, and waiting times between major flares. Statistical methods, including correlation and regression analyses, Cox proportional hazards modeling, and ARIMAX time-series forecasting, were used to investigate relationships between solar conditions and flare …

Models For Second Courses In Data Science: Insights From Five Different Institutions, Ben Baumer, Gillian D. Beltz-Mohrmann, Brianna C. Heggeseth, Nicholas J. Horton, Leslie Myint, Michael A. Posner, Paul Roback

Statistical and Data Sciences: Faculty Publications

As data science continues to mature as an academic discipline, curricular standardization remains elusive. While a number of groups have defined program-level learning outcomes for data science, there is no consensus surrounding course-level learning outcomes. We present five different models for a second course in data science, each of which comes with its own learning goals, coverage of topics, points of emphasis, prerequisites, and role in our respective (statistics and) data science curricula. We use the data science topic, subtopic, and competency/concept framework created by the MASDER project to analyze the content of our courses, and present areas of similarity …

A Geometric Framework For Modeling Real-World Experience, Knowledge, Learning, And Memory, Paxton C. Fitzpatrick

Dartmouth College Ph.D Dissertations

A growing body of recent work highlights the importance of studying learning and memory in “naturalistic” contexts that reflect how these processes function in our day-to-day lives. Yet the incredible complexity of the world around us, and of the idiosyncratic ways we learn from and remember it, make characterizing the dynamics of learning and memory for real-world experiences challenging. I present a computational framework that leverages the embedding representations of language models to cast experiences and memories as high-dimensional trajectories unfolding through a common representation space, enabling them to be directly related and compared via their latent semantic geometries. I …

Mapping St. Louis History: Slavery, Redlining, And The May 16th Tornado, Safa M. Altamimi, Aster Horbanova, Riley J. Bearden, Vivian B. Rhodes, Bridget Ragan

Undergraduate Research Symposium

For this project, our group members are traveling across St.Louis to areas that are affected by the May 16th tornado. We will be taking pictures and using ArcGIS survey123 to input data connecting the tornado to historical forces such as Redlining and Slavery. Using this data, we will be creating an ArcGIS StoryMap and presenting our findings.

Mathematical Modelling For Optimizing Predator–Prey Networks In A Resilient Wildlife Ecosystem: A Systematic Literature Review, Thadei Damas Sagamiko

Tanzania Journal of Science

The resilience of wildlife networks hinges on the adaptability and stability of complex predator-prey interactions. Mathematical modelling offers a crucial approach for examining and optimizing these dynamics under biological invasions and environmental pressures. This systematic literature review (SLR) employs a bibliometric analysis approach to synthesize global research on mathematical models applied to predator-prey network optimization, with an emphasis on their ecological implications, computational approaches, and modelling strategies. Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), 50 peer- reviewed studies (2000–2025) were analyzed using the Scopus and Google Scholar databases. The study identifies classical models, such as the …

Clustering Of Potable Water Sources In Rural Oyo State, Nigeria: Implications For Equitable Distribution And Sustainability, Adesola Adediran, Ibrahim O. Raufu

Tanzania Journal of Science

Adequate and equitable access to potable water remains a major challenge in rural communities of Oyo State, Nigeria, with uncoordinated provision leading to widespread shortages. This study employs geospatial techniques to assess the spatial distribution of water supply sources across five purposively selected rural communities in Olorunsogo Local Government Area (OLGA). Geographic coordinates of boreholes, hand-dug wells, and other potable water sources were collected using a handheld Garmin GPS 76S device. Spatial analysis was conducted using ArcGIS, and Nearest Neighbour Analysis (NNA) was applied to evaluate the pattern of distribution. Results indicate a strong tendency toward clustering, with a Nearest …

Graph-Theoretic Analysis Of Function Call Graphs Using L-Moments For Malware Detection, Anthony J. Rose, Wayne C. Henry, Scott R. Graham, Christine M. Schubert Kabban, Christopher M. Rondeau

Faculty Publications

Most malware detection continues to rely on signature matching, which struggles with obfuscated or novel variants. This research presents a malware detector that instead relies on the static structure of function call graphs, summarized using L-moments of the degree, betweenness, closeness, and clustering measures into compact, interpretable descriptors that persist across malware variants. Evaluated on 175,633 binaries spanning many families under balanced, natural-prevalence, and prevalence-weighted training regimes, ensemble methods achieved Area Under the Curve (AUC) values up to 0.98 on the full-corpus random split, while a feed-forward neural network remained robust across regimes with AUC 0.92–0.95. Linear baselines were far …

When Three Points Aren't Enough: Parameter Estimation In Newton's Law Of Cooling, Alberto A. Condori, Cara D. Brooks, Madeline R. Goldberg

CODEE Journal

We begin with a paradoxical three-point problem where the standard parameter estimation formula fails because temperature data must satisfy a concavity condition reflecting Newton's Law of Cooling's exponential structure. Although a closed-form solution for A exists for equally-spaced measurements, high sensitivity to error motivates the use of overdetermined systems with many measurements. The "profiling over A" technique transforms this three-parameter nonlinear problem into a sequence of simple linear regressions, providing computational efficiency and conceptual transparency. This approach can be generalized to many parameter estimation problems in science and engineering, making it a valuable tool for undergraduates interested in applied mathematics. …

Analysis Of The Influence Of Anthropometric Dimensions Of Postural Ergonomics Using Multiple Linear Regression, Gashbeen Faisal Najmaddin, Edrees Muhammed Tahir Harki

Al-Bahir

Ergonomics is the study of designing and arranging a workspace or product to optimize the “fit” between people and their work, ensuring safety, comfort, and efficiency. The scientific literature indicates that ergonomic perspectives on the workplace are connected to the anthropometrics of societies. This study primarily aims to create a model that integrates multiple predictive variables to estimate the target variable. Multiple linear regression analysis is used to identify how different anthropometric dimensions predict postural ergonomics during prolonged sitting. Based on the regression models’ results, the predicted can be calculated using user anthropometry and existing chair dimensions. Furthermore, the primary …

A Database Of Reliability-Targeted Snow Loads For The United States, Nicholas Brimhall, Cameron Swapp, Brennan Bean

Browse all Datasets

This data release provides the data and code necessary to create a database of 10,625 site-specific Reliability-Targeted Snow Loads (RTSLs) for locations in the Conterminous United States and Alaska. RTSLs are the design snow load requirements necessary to achieve the reliability targets set forth in Chapter 1 of the ASCE 7 Standard, which is the national standard that largely governs hazards-based design in the United States. The data and methods used in this data release are based upon the methods described in Bean et al. (2021), with updated data and refined methodology as applicable.

A Global Database Of Winter Wind Values, Jayden Gardner, Hannah Fluckiger, Nicholas Brimhall, Brennan Bean

Browse all Datasets

This repository contains the data and code necessary to create winter wind (W2) estimates for international locations. It is an extension of the approach outlined in Bean et al. (2025) to include additional data from European locations. Winter wind is defined as the proportion of time that the wind speed exceeds 10 mph during the months of October to April, which is used in snow load design to calculate the potential severity of drifting on a roof.

Base Maps And Other Source Files For: A Database Of Reliability-Targeted Snow Loads For The United States, Nicholas Brimhall, Cameron Swapp, Brennan Bean

Browse all Datasets

This data release provides the supporting data and files to create a database of 10,625 site-specific Reliability-Targeted Snow Loads (RTSLs) for locations in the Conterminous United States and Alaska (see Brimhall et al. (2026)). RTSLs are the design snow load requirements necessary to achieve the reliability targets set forth in Chapter 1 of the ASCE 7 Standard, which is the national standard that largely governs hazards-based design in the United States. The data and methods used in this data release are based upon the methods described in Bean et al. (2021), with updated data and refined methodology as applicable.