Statistics and Probability Commons^™

Human Endogenous Retroviruses (Hervs) Associated With Glioblastoma Risk And Prognosis, Harun Mazumder, Hui Yi Lin, Melody Baddoo, Wojciech Gałan, Diana Polania-Villanueva, Chindo Hicks, David Otohinoyi, Francesca Peruzzi, Zbigniew Madeja, Victoria P. Belancio, Erik K. Flemington, Krzysztof Reiss, Monika Rak

School of Medicine Faculty Publications

Emerging evidence suggests expression from human endogenous retrovirus (HERV) loci likely contributes to, or is a biomarker of, glioblastoma multiforme (GBM) disease progression. However, the relationship between HERV expression and GBM malignant phenotype is unclear. Applying several in silico analyses based on data from The Cancer Genome Atlas (TCGA), we derived a locus-specific HERV transcriptome for glioma that revealed 211 HERVs significantly dysregulated in the comparisons of GBM vs. normal brain (NB), GBM vs. low-grade glioma (LGG), and LGG vs. NB. Our analysis supported development of a unique HERV scoring algorithm that segregated GBM, LGG, and NB. Interestingly, lower HERV …

Hybrid Graph-Recurrent Architecture For Citation Recommendation Via Future Embedding Forecasting, Mohammad Ausaf Ali Haqqani

Computer Science and Engineering Theses and Dissertations

The rapid expansion of scientific literature has intensified the challenge of identifying relevant citations, particularly for newly published or under-cited papers. Traditional citation recommendation systems typically model static relationships or respond to past citation activity, offering limited predictive power for emerging works. In response, this thesis presents a temporal modeling framework for citation recommendation that anticipates future scholarly relevance by forecasting the latent representations of academic papers.

Building on prior work that utilized Temporal Graph Networks (TGNs) to model dynamic citation flows, we propose Graph-Time, a hybrid architecture that integrates a Graph Transformer with a GRU-based time series predictor. The …

“Do You Even Lift, Bro?”: Correlates Of Muscle Dysmorphia Symptomatology In Filipino Male University Students, Pamela Paula C. Pioquinto

UNLV Theses, Dissertations, Professional Papers, and Capstones

Muscle Dysmorphia (MD) is a subtype of Body Dysmorphic Disorder (BDD) and is marked by the desire to increase muscularity and reduce body fat. MD is typically more prevalent among younger male populations, and it often drives comorbid disorders, including substance abuse, eating disorders, and social anxiety. Despite the growing literature on MD, it remains understudied in certain racial/ethnic populations, such as Filipinos. Acculturation, defined as the process in which an individual adopts, acquires, and adapts to a new cultural environment as a result of immigration, influences body image by reshaping an individual’s perceptions of beauty and muscularity standards. Guided …

Associations Of The Medicaid Expansion Policy With Racial/Ethnic Disparities In Breast Cancer Screening And Treatment, James Howard Smith Ii

UNLV Theses, Dissertations, Professional Papers, and Capstones

Background and Aim. This dissertation examines Medicaid expansion’s effect on breast cancer (BC) screening and treatment disparities in two Medicaid-expanding states (MES) that passed Medicaid expansion (New Jersey and Vermont) relative to two non-expansion states (NES) (Georgia and Wisconsin). It then compares the association of Medicaid expansion to other states that did not undertake this policy on various subgroups, such as minorities by race and ethnicity, income, and age groups. Black women still bear significant BC disparities due to untimely screenings, treatments, and deaths. Therefore, it is critical to investigate how socioeconomic status influences racial/ethnic disparity gaps, given that BC …

Resale Revolution: Trend Implications From Media Presence Transcended To Luxury Retail Markets, Penelope Prochnow

Capstone Projects

This study aims to deepen understanding of fashion trend decline from peak popularity to obsolescence, with implications for sustainability and producer profit margins. It investigates how the attributes and media presence of fashion items influence their journey from high-end editorial coverage to resale platforms. Using survival analysis to model trend lifetimes and cosine similarity metrics to compare resale and magazine keyword frequencies, alongside machine learning for price prediction, the study uncovers critical temporal patterns. Results show that resale trends reflect magazine content with a lag of approximately 18 to 30 months and draw from long-wave revivals spanning 6 to 14 …

On The H-Property For Step-Graphons: Residual Case, Wanting Gao

McKelvey School of Engineering Graduate Student Theses & Dissertations

We investigate the H-property for step-graphons. Specifically, we sample graphs Gn on n nodes from a step-graphon and evaluate the probability that Gn has a Hamiltonian decomposition in the asymptotic regime as n → ∞. It has been shown in Belabbas and Chen (2023); Belabbas et al. (2021) that for almost all step-graphons, this probability converges to either zero or one. We focus in this paper on the residual case where the zero-one law does not apply. We show that the limit of the probability still exists and provide an explicit expression of it. We present a complete proof of …

Challenges Us Study Abroad Students Face When Acculturating To A Foreign Food Environment, Sydney C. Davis

Honors Theses

Studying abroad presents a unique opportunity for college students to immerse themselves in new cultures, including experiencing different food environments and dietary customs. However, adapting to a foreign food culture can pose various challenges that impact students’ overall health, eating behaviors, and study abroad experiences. The purpose of this study to develop a survey instrument designed to investigate the extent to which U.S. study abroad students embrace, accept, and face challenges with different aspects of Italian food culture, using the Social Ecological Model (SEM) as a guiding framework. Initial survey items were developed with information obtained from focus group discussions …

Evaluating Predictive Models For Predicting Total Score Of Beef Carcasses, Emmanuel Forson

Electronic Theses and Dissertations

The beef industry plays a vital role in global agriculture, with carcass quality and consumer preference being key determinants of market success. This thesis examines predictive modeling techniques for estimating the Total Score of beef carcasses, a composite measure representing yield and quality, primarily used by the Nebraska Cattlemen Association. Using data from the Nebraska Cattlemen’s Foundation Retail Value Steer Challenge (2000–2023), the study compares the performance of First Order Multiple Linear Regression (MLR) with three machine learning techniques: K-Nearest Neighbors (KNN), Random Forest, and Gradient Boosting Machine (GBM).

The analysis focuses on six key predictors: Hot Carcass Weight, Back …

Bayesian Statistics: Origins And Applications, Evelyn Pulla

Publications and Research

Bayesian Statistics applies Bayes' Theorem to update beliefs through new evidence. In this project, I explored how Bayesian Statistics applies into real supporting decision-making under uncertainty. By solving problems using data, I was able to realize how prior knowledge and evidence collaborate to make better conclusions. The project also demonstrates how Bayesian reasoning corrects our intuition to make decisions based on logical reasoning. Through this project, I was able to learn why using probability to make informed decisions matters both in science and real life.

Statistics - What Does My Data Say About Me?, Taylor Gadsden-Deterville

Student Scholar Symposium Abstracts and Posters

For my Introduction to Statistics Class, I have been tasked with collecting unique, personal data to give insight into my daily routine. I decided to record nine different outcomes (two qualitative and seven quantitative). On February 6, 2025, I began with a blank Excel sheet, and so far, I have 57 full days of data collected. I will continue monitoring my findings for the remainder of the Spring 2025 Semester. Per my project instructions, I must include tables and graphs for my qualitative and quantitative outcomes. So far, I have collected daily quantitative data on my screen time (Instagram and …

Using A Pharmacokinetic Model To Design And Evaluate An Early Ctdna Biomarker For Response To Targeted Therapy, Aaron Li

Spora: A Journal of Biomathematics

Early prediction of response to therapy or lack thereof can help physicians plan treatment more efficiently. Biomarkers based on circulating tumor DNA (ctDNA) are promising. However, biomarkers beyond direct comparison to baseline have not been thoroughly explored. We develop a model for ctDNA shedding under targeted therapy that incorporates pharmacokinetics. Using a simulated cohort of virtual patients with varied parameters, we define and analyze a biomarker based on ctDNA samples at baseline, 12 hours, and 24 hours after initiation of treatment. The biomarker identified patients who would achieve partial or complete response with high sensitivity and specificity and was able …

Two-Sample Bi-Directional Causality Between Two Traits With Some Invalid Ivs In Both Directions Using Gwas Summary Statistics, Siyi Chen

School of Public Health Faculty Publications

Mendelian randomization (MR) is a widely used method for assessing causal relationships between risk factors and outcomes using genetic variants as instrumental variables (IVs). While traditional MR assumes uni-directional causality, bi-directional MR aims to identify the true causal direction. In uni-directional MR, invalid IVs due to pleiotropy can violate assumptions and introduce biases. In bi-directional MR, traditional MR can be performed separately for each direction, but the presence of invalid IVs poses even greater challenges. We introduce a new bi-directional MR method incorporating stepwise selection (Bidir-SW) designed to address these challenges. Our approach leverages public genome-wide association study (GWAS) datasets …

Modeling Literary Connections: Exploring Transregional Resistance In Dalit Poetry, Antara Bhattacharyay

Mathematics, Statistics, and Computer Science Honors Projects

Structuring socio-political identities, the caste system (a graded form of hierarchy) remains entrenched in contemporary Indian society. Dalits, marginalized by the caste system, have expressed their resistance through literature, envisioning substantive equality and social change. In this thesis, I draw on digital humanities methods to examine regional variation in translated Dalit poetry from Bengali, Hindi/Urdu, Marathi, and Tamil languages. I utilize topic modeling—a machine learning algorithm that detects latent semantic structures in a text—as a point of departure for poetry analysis. I observe how topic modeling enables newer readings of the poems, revealing regionally resonant and broader Dalit themes.

Contribution Of Various Factors On The Rate Of Traffic Accidents In The Us, Martin Mnatsakanyan

Undergraduate Research Symposium Lightning Talks

Background:

Until 2020, the number of traffic accidents has been steadily decreasing. After 2020, the number started increasing until 2022, then started s lowly decreasing again. Most drivers aren’t fully aware of the reason behind all of these accidents.

Variable Selection In Mixture Cure Models Using Elastic Net Penalty: Application To Covid-19 Data, Aluwani Ramalata, Akim Adekpedjou, Maseka Lesaoana

Mathematics and Statistics Faculty Research & Creative Works

In survival analysis, it is often assumed that all individuals will eventually experience the event of interest if followed long enough. However, in many real-world scenarios, a subset of individuals remains event-free indefinitely. For instance, in clinical studies, some patients never relapse and are considered cured rather than censored. Traditional survival models are inadequate for capturing this heterogeneity. Mixture cure models address this limitation by distinguishing between cured and susceptible individuals while modeling the survival of the latter. A key challenge in mixture cure modeling is selecting relevant covariates, particularly when dealing with time-varying effects. This study develops a penalized …

Latent Variable Dyadic Regression Models For Predicting Over/Under Bets In Sports Betting, Alexcia Trejo

Graduate Theses and Dissertations

This thesis explores the use of latent factor models to uncover hidden structures in pair wise outcomes derived from Over/Under betting markets in sports betting. Specifically, we implement and evaluate the Eigen model, a latent space model that represents dyadic data using node-specific vectors whose inner product govern edge probabilities. By modeling relationships between teams as adjacency matrices of binary outcomes, we investigate the extent to which the Eigen model captures both homophily, the tendency of similar teams to yield consistent betting results, and stochastic equivalence, where different teams exhibit indistinguishable patterns of Over/Under outcomes. A Bayesian formulation of the …

The Impact Of Maternal Age On The Expression Of Transgenerational Plasticity In Daphnia Pulicaria, Calvin Nguyen

2025 Spring Honors Capstone Projects - Archive

Transgenerational plasticity refers to heritable, non-genetic changes in phenotype that persist across multiple generations and can enhance offspring survivability in variable environments. In Daphnia, increasing maternal age has been associated with maladaptive plasticity. To investigate this relationship, six clones were collected from two Wisconsin lakes and acclimated to laboratory conditions through a common garden rearing process. For each clone, ten replicates were generated and evenly divided between young (clutches 2–4) and old (clutches 5–8) maternal age groups. Offspring were exposed to three dietary treatments for three experimental generations: one fed only green algae, one fed only cyanobacteria (a nutritionally …

Aleci: An R Package For Non-Parametric Confidence Intervals On Accumulated Local Effects Plots, Matthew R. Lister

All Graduate Reports and Creative Projects, Fall 2023 to Present

Machine learning models can take a collection of inputs and craft an output. The mathematical formulas these models use to calculate their outputs easily become too complex or time consuming for a human to analyze. Collectively, we refer to these as black box models. Accumulated local effects plots (ALE) are a method for adding interpretability and visibility into the effects that individual variables contribute to the predictions made by black box models. The method designed by D.W. Apley calculates equally spaced point estimates of the response value to construct a graph across the range of the variable of interest. AleCI …

New Bayesian Methods For Longitudinal Data Analysis With Complex Dependence Structures., Anish Mukherjee

Electronic Theses and Dissertations

Longitudinal data in real-world settings are frequently found to be heterogeneous and exhibit intricate spatio-temporal dependence structures. Analyzing such complex data to obtain reliable estimation while quantifying uncertainty necessitates using sophisticated Bayesian methodology. In this work, we present novel Bayesian methods developed to address these challenges. We often observe heterogeneity in longitudinal data, where the mean and variance for certain profiles meaningfully differs from the rest. Some profiles may also exhibit outliers at a limited number of measurements. Using a standard mixed effects model, which assumes homogeneity, can lead to overestimating the residual variance and inefficient estimation. In this work, …

Statistical Designs For Learning User Preference And Experience, William S. Fisher

All Dissertations

For any organization, designing a new product or planning the features of a new service is a complex decision-making process which depends on understanding its users' preferences and the experiences they have with prototypes of the new product or service. Design of experiments (DOE) offers a framework to aid in the modeling and collection of data to learn user preferences and experiences to facilitate this decision-making process. Questions such as ``which of our newly proposed products is preferred most by our customers" and ``which version of our new service results in the highest monthly revenue" can be readily addressed by …