Statistics and Probability Commons^™

Aspirations Incubator: 2017–2023 Final Evaluation Report, Sarah Goan, Elora Way, Emma Schwartz, Becky Wurwarg

Publications

The Rural Futures Fund launched the Aspirations Incubator in 2017 as a six-year pilot initiative aimed at supporting youth development in rural Maine by increasing resiliency, fostering connections to communities, and expanding students’ awareness of opportunities beyond their everyday environments. The program partnered with select rural youth organizations to build capacity for mentoring-based programs serving students from seventh grade through high school graduation. Guided by the Trekkers Youth Programming Principles, the initiative focused on long-term mentorship, meaningful relationship-building, and community engagement. A comprehensive multi-year evaluation conducted by the Data Innovation Project at the University of Southern Maine examined the implementation …

Automatic Appraisals Of Houses, Sloan Scroggin

Graduate Theses and Dissertations

Multiple hedonic models and an automatic appraiser model were used to create a residential house’s estimated sales price. The goal is to use the limited data available to a REALTOR® to estimate the future sales price of a residential home without the aid of pictures of the property or viewing the physical property. The first model automates some of the actions of an appraiser by finding comparable sales based on proximity, based both on distance between houses and characteristics of the houses, and then calculating a weighted average price for an estimated sales price of future sales. If the model …

Advancement Of Iterative Optimization Technology Algorithms Toward Calibration-Free Process Analytical Technology Applications, Adam Rish

Electronic Theses and Dissertations

The expansion of spectroscopic process analytical technology (PAT) tools within the pharmaceutical industry has the potential to elevate the current state-of-the-art of pharmaceutical manufacturing by offering opportunities for reduced quality testing times, enhanced process control, and greater production flexibility. Spectroscopic PAT tools are dependent on multivariate models to extract the relevant information from the spectral outputs. However, there is a substantial calibration burden for developing and maintaining these multivariate models that discourages the application of PAT, despite the encouragement from regulators. This has led to an interest in calibration-free methods such as iterative optimization technology (IOT) for spectroscopic PAT that …

An Improved Bayesian Pick-The-Winner (Ibpw) Design For Randomized Phase Ii Clinical Trials, Wanni Lei, Maosen Peng, Xi K. Zhou

COBRA Preprint Series

Phase II clinical trials play a pivotal role in drug development by screening a large number of drug candidates to identify those with promising preliminary efficacy for phase III testing. Trial designs that enable efficient decision-making with small sample sizes and early futility stopping while controlling for type I and II errors in hypothesis testing, such as Simon’s two-stage design, are preferred. Randomized multi-arm trials are increasingly used in phase II settings to overcome the limitations associated with using historical controls as the reference. However, how to effectively balance efficiency and accurate decision-making continues to be an important research topic. …

Spatiotemporal Negative Inventory Outlier Decomposition For Supply Chain Applications In Consumer-Packaged Goods (Cpg), Hayden Mcdonald

Data Science Undergraduate Honors Theses

Coca-Cola is a popular soft drink brand with sales occurring in every Walmart store across the world, which generates large quantities of data and requires a robust supply chain system. However, the company does not currently have a sophisticated, automated, and/or prescriptive system for detecting where, when, and why inventory outages occur and applying preventative measures to avoid loss of revenue from the absence of inventory on store shelves. This thesis proposes and applies a novel, prescriptive system for this purpose. An inventory outage can be seen as a ‘negative’ statistical outlier in a time series of inventory for an …

Modeling Prices In Limit Order Book Using Univariate Hawkes Point Process, Wenqing Jiang

LSU New Orleans Theses and Dissertations

This thesis presents a time-changed geometric Brownian price model with the univariate Hawkes processes to trace the price changes in a limit order book. Limit order books are the core mechanism for trading in modern financial markets, continuously collecting outstanding buy and sell orders from market participants. The arrival of orders causes fluctuations in prices over time. A Hawkes process is a type of point process that exhibits self-exciting behavior, where the occurrence of one event increases the probability of other events happening in the near future. This makes Hawkes processes well-suited for capturing the clustered arrival patterns of orders …

Examining Award Compliance To Inform Resource Allocation, Jacob Haarala

Data Science Undergraduate Honors Theses

This project focuses on JB Hunt Transport Inc's intermodal business unit (JBI) by focusing on the challenges associated with Published Pricing and Contractual Pricing. The primary issue revolves around the variance between the awarded freight volumes in Requests for Pricing (RFPs) and the actual volumes realized when the freight is shipped. This discrepancy poses challenges for effective sales planning, revenue goals, and optimal freight network management within JBI. Reporting tools, such as PowerBI, are currently used by JBI to provide insights into award compliance on a weekly basis. However, our goal with this project was to provide a deeper understanding …

Cost-Risk Analysis Of The Ercot Region Using Modern Portfolio Theory, Megan Sickinger

Master's Theses

In this work, we study the use of modern portfolio theory in a cost-risk analysis of the Electric Reliability Council of Texas (ERCOT). Based upon the risk-return concepts of modern portfolio theory, we develop an n-asset minimization problem to create a risk-cost frontier of portfolios of technologies within the ERCOT electricity region. The levelized cost of electricity for each technology in the region is a step in evaluating the expected cost of the portfolio, and the historical data of cost factors estimate the variance of cost for each technology. In addition, there are several constraints in our minimization problem to …

Evaluating Novel Approaches For Improving Anadromous Fish Passage In Coastal Rivers, Aaron Bunch

All Dissertations

Few of the world's rivers remain free-flowing for over 1000 km, significantly impacting anadromous fish populations due to dam-induced habitat fragmentation. Three partial migration barriers (low-head dams) on North Carolina's Cape Fear River (USA)—lock and dam 1 (LD1), lock and dam 2 (LD2), and lock and dam 3 (LD3)—impact American shad Alosa sapidissima, Atlantic sturgeon Acipenser oxyrinchus oxyrinchus, and striped bass Morone saxatilis populations. Mitigation strategies to pass fish upstream have included conservation locking, nature-like fishway construction, and environmental flows (e-flows). This study introduced a novel e-flow (dam submergence flow), tested acoustic double-tagging techniques for tracking American …

Statistical Methodologies For Sequential Online Controlled Experiments, Yangyi Li

All Dissertations

Online controlled experiments, primarily used on digital platforms like websites or apps, involve varying certain variables while keeping others constant to determine their effects on specific outcomes. This method allows researchers to compare results with a control group, gaining reliable insights. These experiments have grown popular among companies for assessing product impacts and guiding decision-making.

This dissertation presents a group Sequential Probability Ratio Testing (group SPRT) algorithm optimized for online experiments to balance sample number and accuracy by minimizing expected costs. It contrasts group SPRT with traditional SPRT under normal distributions, revealing differences in test power, sample size, and cost …

Per- And Polyfluoroalkyl Substance Exposure Risks In Us Carceral Facilities, 2022, Lindsay Poirier, Derrick Salvatore, Phil Brown, Alissa Cordner, Kira Mok, Nicholas Shapiro

Statistical and Data Sciences: Faculty Publications

Objectives. To assess the US incarcerated population’s risk of exposure to per- and polyfluoroalkyl substances (PFASs). Methods. We assessed how many of the 6118 US carceral facilities were located in the same hydrologic unit code watershed boundaries as known or likely locations of PFAS contamination. We conducted geospatial analyses on data aggregated from Environmental Protection Agency databases and a PFAS site tracker in 2022 to model the hydrologically feasible known and presumptive PFAS contamination sites for nearly 2 million incarcerated people. Results. Findings indicate that 5% (∼310) of US carceral facilities have at least 1 known source of PFAS contamination …

Landslide Susceptibility And Tree Ring Eccentricity Analysis Along Unstable Slopes Of The New River Watershed, Anderson And Morgan Counties, Tn, Megan Palmer

Electronic Theses and Dissertations

Landslides are mass movements that affect infrastructure across East Tennessee, causing problems for the Tennessee Department of Transportation (TDOT). An assessment of conditions and locations of unstable slopes can aid TDOT in infrastructure management. Landslide susceptibility was evaluated for Anderson and Morgan counties, TN, off State Route 116 in the New River watershed. Susceptibility maps used a landslide inventory and six factors: elevation, slope, geology, distance from stream, rainfall, and curvature, input in forest-based classification and logistic regression models. Additionally, affected trees along these unstable slopes in Anderson and Morgan counties were cored to analyze mass movement impacts on tree …

An Analysis Of Lyrical Repetition And Popularity In Popular Music Genres, Josh White

Undergraduate Honors Capstone Projects

This paper examines the correlation between repetitiveness and popularity in the genres of Christian, Country, EDM, Hip-Hop, Latin, Pop, R&B, and Rock. Repetitiveness is defined by the frequency of repeated words in lyrics, and the average number of streams per day defines popularity. This analysis also acknowledges the "popularity" metric provided by Spotify in calculating the correlation. To calculate this correlation, I wrote a program that accesses the Spotify and Genius APIs to gather metadata related to 76,069 songs from 1,246 artists, including data on repetitiveness, tempo, duration, and Spotify's audio metrics of "danceability," "energy," "speechiness," "acousticness," and "instrumentalness." I …

Using The History Of Statistics To Teach Introductory Statistics, Melissa Hansen

All Graduate Reports and Creative Projects, Fall 2023 to Present

While often taught in high school and required as part of a college degree, statistics classes are sometimes viewed by students as an obstacle rather than a support for their overall goals. One way to increase student engagement in a statistics course is to use the history of statistics. Within the literature review, the advantages to using the history of statistics are discussed as well as the more extensive research on using the history of mathematics in mathematics courses. Included are instructional strategies for using the context around the development of mathematical ideas in math classrooms which can be extended …

The Future Of Brain Tumor Diagnosis: Cnn And Transfer Learning Innovations, Shengyuan Wang

Mathematics, Statistics, and Computer Science Honors Projects

For the purpose of improving patient survival rates and facilitating efficient treatment planning, brain tumors need to be identified early and accurately classified. This research investigates the application of transfer learning and Convolutional Neural Networks (CNN) to create an automated, high-precision brain tumor segmentation and classification framework. Utilizing large-scale datasets, which comprise MRI images from open-accessible archives, the model exhibits the effectiveness of the method in various kinds of tumors and imaging scenarios. Our approach utilizes transfer learning techniques along with CNN architectures strengths to tackle the intrinsic difficulties of brain tumor diagnosis, namely significant tumor appearance variability and difficult …

Exploring Application Of The Coordinate Exchange To Generate Optimal Designs Robust To Data Loss, Asher Hanson

All Graduate Theses and Dissertations, Fall 2023 to Present

The primary objective of this study is to evaluate the efficacy of the coordinate exchange (CEXCH) algorithm in the generation of robust optimal designs. The assessment involves a comparative analysis, wherein designs produced by the Point Exchange (PEXCH) Algorithm are employed as benchmarks for evaluating the efficiency of CEXCH designs. Three modified criteria, selected from the traditional alphabet criteria pool, are utilized to score each algorithm. To enhance the reliability of the comparative analysis, multiple rounds of validation are conducted, focusing on visual assessments, design scores, and criteria efficiencies. The findings from each round of validation contribute to a comprehensive …

Employing Digital Pcr For Enhanced Detection Of Perinatal Toxoplasma Gondii Infection: A Cross-Sectional Surveillance And Maternalinfant Outcomes Study In El Salvador, Mary K. Lynn, Marvin Stanley Rodriguez Aquino, Pamela Michelle Cornejo Rivas, Xiomara Miranda, David F. Torres-Romero, Hanson Cowan, Madeleine M. Meyer, Willber D. Castro-Godoy, Mufaro Kanyangarara Ph.D., Stella C.W. Self Ph.D., Ms, Berry A. Campbell, Melissa S. Nolan Ph.D., Mph

Faculty Publications

Toxoplasma gondii is a parasitic infection that can be transmitted in utero, resulting in fetal chorioretinitis and other long-term neurological outcomes. If diagnosed early, pregnancysafe chemotherapeutics can prevent vertical transmission. Unfortunately, diagnosis of acute, primary infection among pregnant women remains neglected, particularly in low-andmiddle-income countries. Clinically actionable diagnosis is complex due to the commonality of infection during childhood and early adulthood which spawn long-last antibody titers and historically unreliable direct molecular diagnostics. The current study employed a cross-sectional T. gondii perinatal surveillance study using digital PCR, a next generation molecular diagnostic platform, and a maternal-fetal outcomes survey to ascertain the …

Descriptions Of Interglacial Mastodons From Snowmass, Colorado, Connor White

Electronic Theses and Dissertations

The Ziegler Reservoir fossil site (ZRFS) in Colorado contains over 4000 mastodon bones that date from 140,000 to 100,000 years ago. At an elevation of ~2705 meters above sea level, ZRFS represents an alpine ecosystem dated to Marine Isotope Stage (MIS) 5. Formal descriptions of cheek teeth, mandibles, crania, and femora were completed. Statistical analyses of the upper and lower third molars, including a novel measurement of interloph(id) distances, indicate significant differences between ZRFS mastodons and Mammut pacificus, while falling within the ranges for Mammut americanum. This study agrees with the taxonomic assignment of ZRFS mastodons to Mammut …

The Forget Time For Random Walks On Trees Of A Fixed Diameter, Lola R. Vescovo

Mathematics, Statistics, and Computer Science Honors Projects

A mixing measure is the expected length of a random walk on a graph given a set of starting and stopping conditions. We study a mixing measure called the forget time. Given a graph G, the pessimal access time for a target distribution is the expected length of an optimal stopping rule to that target distribution, starting from the worst initial vertex. The forget time of G is the smallest pessimal access time among all possible target distributions. We prove that the balanced double broom maximizes the forget time on the set of trees on n vertices with diameter …

Selected Topics On Sequential Designs For Decision Making, Caroline Kerfonta

All Dissertations

This dissertation is comprised of three parts. The first proposes a sequential approach to determine the experimental setting with the minimum variance (Kerfonta et al., 2024). Two acquisition functions are developed to assist developing the approach. Theoretical results along with a case study using data from crystallization experiments is conducted to show the ability of the proposed method to correctly select the experiment with the minimum variance. The second and third parts propose adaptations to the Bayesian optimization algorithm using transformed additive Gaussian processes (TAG) as the surrogate model. The goal of using the TAG framework is to decompose the …

High-Dimensional Mediation Analysis Of Multi-Omics Data, Sunyi Chi

Dissertations and Theses (Open Access)

Environmental exposures such as cigarette smoking influence health outcomes through intermediate molecular phenotypes, such as the methylome, transcriptome, and metabolome. Mediation analysis is a useful tool for investigating the role of potentially high-dimensional intermediate phenotypes in the relationship between environmental exposures and health outcomes. Rapid development of high-throughput technologies have made mediation analysis of multi-omics data critical to gain groundbreaking insights into the biological mechanisms underlying the disease etiology. This dissertation aims to develop mediation analysis methods that utilize the enormous amount of multi-omics data in assessing mechanisms of disease etiology. It contains three projects where I propose advanced mediation …

A Manufacturing-To-Response Pathway For Manufacturing Optimization Of Carbon Fiber Reinforced Polymer Composite Structures, Madhura Limaye

All Dissertations

Over the past decade, there has been an increased adoption of thermoplastic and thermoset based continuous carbon fiber reinforced polymer (CFRP) composites for structural applications in several industries. Among the different manufacturing methods, thermoforming process for thermoplastic based continuous CFRP’s offer a major advantage in reducing cycle times for large scale productions. Similarly, out-of-autoclave curing process for thermoset based continuous CFRP’s using heated tooling enables production of large composite structures. However, these manufacturing processes can have a significant impact on the structural performance of parts by inducing undesirable effects. These effects include inhomogeneous fiber orientations, thickness variations, and residual stresses …

Efficient Fully Bayesian Approaches To Brain Activity Mapping With Complex-Valued Fmri Data: Analysis Of Real And Imaginary Components In A Cartesian Model And Extension To Magnitude And Phase In A Polar Model, Zhengxin Wang

All Dissertations

Functional magnetic resonance imaging (fMRI) plays a crucial role in neuroimaging, enabling the exploration of brain activity through complex-valued signals. Traditional fMRI analyses have largely focused on magnitude information, often overlooking the potential insights offered by phase data, and therefore, lead to underutilization of available data and flawed statistical assumptions. This dissertation proposes two efficient, fully Bayesian approaches for the analysis of complex-valued functional magnetic resonance imaging (cv-fMRI) time series.

Chapter 2 introduces the model, referred to as CV-sSGLMM, using the real and imaginary components of cv-fMRI data and sparse spatial generalized linear mixed model prior. This model extends the …

Evaluating Taxonomic Approaches: A Comparative Study Of Educational Frameworks Applied To Mathematics Assessments, Lily Roth

Undergraduate Honors Capstone Projects

The design of effective assessments and reporting of a student’s achievement on learning objectives are often overlooked, leaving educational stakeholders lacking the ability to create meaningful evaluations. To assist in creating substantial mathematics assessments this work seeks to answer the following research questions: ‘How can educational taxonomies be utilized to improve the design of mathematics assessments’? and ‘What are the strengths and weaknesses of applying different taxonomies onto mathematics assessments?’. The purpose of this study is to (1) develop a practical design instrument for easier identification and categorization of assessment questions within each educational taxonomy structure and (2) evaluate the …

Factors Predictive Of The Development Of Surgical Site Infection In Thyroidectomy, A Replication Study Of Myssiorek (2018), Kaitlyn M. Kenig

Capstone Experience: Master of Public Health

The original study aimed to show that thyroidectomy does not result in surgical site infection (SSI) in most cases, and thus routine prescription of antibiotics is not necessary. The study looked to see what risk factors could predict the incidence of SSI. This would highlight those individuals who were at most risk of developing SSI, and then antibiotics would only be prescribed to these individuals instead of all or most individuals who undergo thyroidectomy.

This study used NSQIP data to look at incidence of SSI and look for risk factors that may be predictive of SSI. Only surgeries that were …

Development And Pilot Testing Of A Surface Discrimination Test For People With Lower Limb Amputation, Colin Kruger, Kyle Mcknight, Sharlene Lim, Samuel Straus

UNLV Theses, Dissertations, Professional Papers, and Capstones

Introduction: There is a lack of understanding as to how sensory loss and sensory deficits impact those with LLA. The purpose of this research is to determine the extent to which people with LLA can discriminate between surfaces underfoot, in order to better understand the relationship between people with LLA and their perception of the ground they are walking on. We developed a test to determine which qualities of surfaces may be easier to distinguish.

Methods: 10 unimpaired adults and 2 adults with LLA participated. Participants compared surfaces underfoot that consisted of ceramic, rough tile, gravel, sand, and sandpaper to …

Detection Of Deficiencies And Data Analysis Of Bridge Members With Deep Convolutional Neural Networks, Bennett Jackson

Department of Civil and Environmental Engineering: Dissertations, Theses, and Student Research

Concrete cracks and structural steel corrosion are two of the most common defects in bridges. Quantifying and classifying these defects provide bridge inspectors and engineers with valuable data for assessing deterioration levels. However, the bridge inspection process is typically a subjective, time intensive, and tedious task, as defects can be overlooked or in locations not easily accessible. Previous studies have investigated deep learning-based inspection methods, implementing popular models such as Mask R-CNN and U-Net. The architectures of these models offer certain advantages depending on the required task. This thesis aims to evaluate and compare Mask R-CNN and U-Net regarding their …

Information Based Approach For Detecting Change Points In Inverse Gaussian Model With Applications, Alexis Anne Wallace

Electronic Theses, Projects, and Dissertations

Change point analysis is a method used to estimate the time point at which a change in the mean or variance of data occurs. It is widely used as changes appear in various datasets such as the stock market, temperature, and quality control, allowing statisticians to take appropriate measures to mitigate financial losses, operational disruptions, or other adverse impacts. In this thesis, we develop a change point detection procedure in the Inverse Gaussian (IG) model using the Modified Information Criterion (MIC). The IG distribution, originating as the distribution of the first passage time of Brownian motion with positive drift, offers …

Code For Care: Hypertension Prediction In Women Aged 18-39 Years, Kruti Sheth

Electronic Theses, Projects, and Dissertations

The longstanding prevalence of hypertension, often undiagnosed, poses significant risks of severe chronic and cardiovascular complications if left untreated. This study investigated the causes and underlying risks of hypertension in females aged between 18-39 years. The research questions were: (Q1.) What factors affect the occurrence of hypertension in females aged 18-39 years? (Q2.) What machine learning algorithms are suited for effectively predicting hypertension? (Q3.) How can SHAP values be leveraged to analyze the factors from model outputs? The findings are: (Q1.) Performing Feature selection using binary classification Logistic regression algorithm reveals an array of 30 most influential factors at an …

Statistical Classification Using Selection And Ranking Methodologies With Statistical Learning, Jeong Jun Lee

UNLV Theses, Dissertations, Professional Papers, and Capstones

The subject of Statistical Classification is concerned with identifying and allocating future observations into one of the pre-categorized classes based on the characteristics of the objects. Typically, these decisions to classify and categorize the objects have been dependent on identifying a system of classification, and from there, determining attributes for sorting.

In past decades, from discriminant analysis, various methods have been developed for classification. In particular, the rise of artificial intelligence (AI), machine learning, and statistical learning theory has made it possible to consider improving the existing methods along with new developments and more comprehensive schemes in conjunction with data-driven …