Statistics and Probability Commons^™

Novel Instance-Level Weighted Loss Function For Imbalanced Learning, Trent Geisler

Doctor of Data Science and Analytics Dissertations

Binary classification using imbalanced datasets remains a challenge. Typically, supervised learning algorithms minimize the binary cross-entropy objective function to determine the final parameter estimates. This objective function assumes an equal class distribution between the minority (i.e. events) and majority (i.e. non-events) classes, which almost never exists in real-world modeling. In the imbalanced data setting, the equal class distribution is grossly violated, and the resulting parameter estimates are biased toward the majority class. To overcome the bias and improve model generalization, we focus on modifying the original binary cross-entropy objective function by uniquely weighting each minority class observation. We base our …

Increasing Perceived Realism Of Objects In A Mixed Reality Environment Using 'Diminished Virtual Reality', Logan Scott Parker

Honors Theses

With the recent explosion of popularity of virtual and mixed reality, an important question has arisen: “Is there a way to create a better blend of real and virtual worlds in a mixed reality experience?” This research attempts to determine whether a visual filter can be created and applied to virtual objects to better convince the brain into interpreting a composite of virtual and real views as one seamless view. The method devised in this thesis is being called 'Diminished Virtual Reality'. The results found in this study show that when presented with a scene composed of a combination of …

The Critical Value Of Maternal And Child Health (Mch) To Graduate Training In Public Health: A Framework To Guide Education, Research And Practice Comment, Julianna Deardorff, Michelle Menser Tissue, Patricia Elliott, Arden Handler, Cheryl Vamos, Zobeida Bonilla, Renee Turchi, Cecilia Sem Obeng, Jihong Liu, Holly Grason

Faculty Publications

Introduction

In light of persistent health inequities, this commentary describes the critical role of maternal and child health (MCH) graduate training in schools and programs of public health (SPPH) and illustrates linkages between key components of MCH pedagogy and practice to 2021 CEPH competencies.

Methods

In 2018, a small working group of faculty from the HRSA/MCHB-funded Centers of Excellence (COEs) was convened to define the unique contributions of MCH to SPPH and to develop a framework using an iterative and consensus-driven process. The working group met 5 times and feedback was integrated from the broader faculty across the 13 COEs. …

Factors Affecting Time To Recovery: A Covid-19 Survival Analysis, Fernanda Montoya

Honors Capstones

This project is focused on the recovery rates of patients diagnosed with COVID-19 after different clinical trial drug treatments. Data for the clinical trial studied was obtained from the National Institute of Allergy and Infectious Diseases for the primary purpose of a survival analysis on patient time to recovery under a placebo and therapeutic drug treatment. Specifically, patients in this clinical trial were randomly selected to receive remdesivir, an antiviral drug, in combination with a placebo or baricitinib, a janus kinase inhibitor drug. Cox PH models were used to identify how the different treatment drugs affect time to recovery and …

Comparative Transcriptomic Study Between Cyanobacteria That Contain Chlorophyll D And Those That Lack Chlorophyll D, Fernanda Montoya

Honors Capstones

All cyanobacteria, which perform oxygenic photosynthesis on Earth, contain the photosynthetic pigment chlorophyll a (Chl a) that absorbs light in the violet and red region of the visible spectrum. Cyanobacteria of the Acaryochloris species, however, contain the rare photosynthetic pigment chlorophyll d (Chl d) that absorbs light in the far-red region. Chl d’s ability to absorb light in this region allows it to avoid competing with other photosynthetic organisms for light. Creating a photosystem that uses Chl d in plants would be of great use for agricultural land optimization, but requires knowledge of the biosynthetic pathways of …

Dietary Score Associations With Markers Of Chronic Low-Grade Inflammation: A Cross-Sectional Comparative Analysis Of A Middle- To Older-Aged Population, Seán R. Miller, Pilar Navarro, Janas M. Harrington, Nitin Shivappa Mbbs, Mph, Ph.D., James Hébert Scd, Ivan J. Perry, Catherine M. Phillips

Faculty Publications

Purpose

To assess relationships between the Dietary Approaches to Stop Hypertension (DASH), Mediterranean Diet (MD), Dietary Inflammatory Index (DII^®) and Energy-adjusted DII (E-DII™) scores and pro-inflammatory cytokines, adipocytokines, acute-phase response proteins, coagulation factors and white blood cells.

Methods

This was a cross-sectional study of 1862 men and women aged 46–73 years, randomly selected from a large primary care centre in Ireland. DASH, MD, DII and E-DII scores were derived from validated food frequency questionnaires. Correlation and multivariate-adjusted linear regression analyses with correction for multiple testing were performed to examine dietary score relationships with biomarker concentrations.

Results

In fully …

Generating A Dataset For Comparing Linear Vs. Non-Linear Prediction Methods In Education Research, Jack Mauro, Elena Martinez, Anna Bargagliotti

Honors Thesis

Machine learning is often used to build predictive models by extracting patterns from large data sets. Such techniques are increasingly being utilized to predict outcomes in the social sciences. One such application is predicting student success. Machine learning can be applied to predicting student acceptance and success in academia. Using these tools for education-related data analysis, may enable the evaluation of programs, resources and curriculum. Currently, research is needed to examine application, admissions, and retention data in order to address equity in college computer science programs. However, most student-level data sets contain sensitive data that cannot be made public. To …

The Efficacy Of The Covid-19 Vaccine In Mississippi, Ilyse Miriam Levy

Honors Theses

The Efficacy of The COVID-19 Vaccine in Mississippi

(Under the direction of Dr. Xin Dang)

By tracking and analyzing fifty-three weeks of COVID-19 data, this thesis analyzes the efficacy of the COVID-19 vaccine within the State of Mississippi. Over the course of these fifty-three weeks, I have also been able to calculate the confidence intervals for vaccination efficacy and the risk reduction due to vaccination by using data regarding the correlations between deaths and vaccination status, provided to me by the Mississippi Office of Epidemiology. My analysis demonstrates that the COVID-19 vaccine is effective not only in Mississippi but also …

Combining Cardiac Monitoring With Actigraphy Aids Nocturnal Arousal Detection During Ambulatory Sleep Assessment In Insomnia, Lara Rösler, Glenn Van Der Lande, Jeanne Leerssen, Austin G. Vandegriffe, Oti Lakbila-Kamal, Jessica C. Foster-Dingley, Anne C.W. Albers, Eus J.W. Van Someren

Mathematics and Statistics Faculty Research & Creative Works

Study Objectives: The objective assessment of insomnia has remained difficult. Multisensory devices collecting heart rate (HR) and motion are regarded as the future of ambulatory sleep monitoring. Unfortunately, reports on altered average HR or heart rate variability (HRV) during sleep in insomnia are equivocal. Here, we evaluated whether the objective quantification of insomnia improves by assessing state-related changes in cardiac measures. Methods: We recorded electrocardiography, posture, and actigraphy in 33 people without sleep complaints and 158 patients with mild to severe insomnia over 4 d in their home environment. At the microscale, we investigated whether HR changed with proximity to …

Forecasting Razorback Baseball Game Outcomes, Austin Raabe

Information Systems Undergraduate Honors Theses

Despite the disappointing end to the 2021 Arkansas Razorback baseball year, the team’s success provided hog fans something to look forward to next season. While they will be without the 2021 Golden Spikes Award winner, Kevin Kopps, and four All-SEC team selections, the 2022 roster has promising new and returning talent. With fifty percent of the players who played significant time last year coming back (minimum ten hits or ten innings pitched), the arrival of several impact transfers from major conferences, and a recruiting class ranked in the top five according to Perfect Game, there is reason to believe that …

How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar

Information Systems Undergraduate Honors Theses

Since the founding of computers, data scientists have been able to engineer devices that increase individuals’ opportunities to communicate with each other. In the 1990s, the internet took over with many people not understanding its utility. Flash forward 30 years, and we cannot live without our connection to the internet. The internet of information is what we called early adopters with individuals posting blogs for others to read, this was known as Web 1.0. As we progress, platforms became social allowing individuals in different areas to communicate and engage with each other, this was known as Web 2.0. As Dr. …

Association Of Phosphate-Containing Versus Phosphate-Free Solutions On Ventilator Days In Patients Requiring Continuous Kidney Replacement Therapy, Melissa L. Thompson Bastin, Arnold J. Stromberg, Sethabhisha N. Nerusu, Lucas J. Liu, Kirby P. Mayer, Kathleen D. Liu, Sean M. Bagshaw, Ron Wald, Peter E. Morris, Javier A. Neyra

Statistics Faculty Publications

Background and objectives Hypophosphatemia is commonly observed in patients receiving continuous KRT. Patients who develop hypophosphatemia may be at risk of respiratory and neuromuscular dysfunction and therefore subject to prolongation of ventilator support. We evaluated the association of phosphate-containing versus phosphate-free continuous KRT solutions with ventilator dependence in critically ill patients receiving continuous KRT.

Design, setting, participants, & measurements Our study was a single-center, retrospective, pre-post cohort study of adult patients receiving continuous KRT and mechanical ventilation during their intensive care unit stay. Zeroinflated negative binomial regression with and without propensity score matching was used to model our primary outcome: …

An Examination Of The Statistics And Risk Management Concepts Behind The Patient Protection And Affordable Care Act (Ppaca) Of 2010, Scott Sinclair

Undergraduate Honors Thesis Collection

The Patient Protection and Affordable Care Act (PPACA) is the overarching federal law that has impacted the intricacies of the health insurance market for more than a decade. Using the supervised learning method of multiple linear regression, the relationship between the medical loss ratio rebates and predictor variables such as the state, health insurance market, and the number of insurance companies owing rebates will be analyzed, along with the actuarial value of metal tiers and geographic rating area factors in terms of their relationship to the insurance premium for a standard family of four, defined as a forty-year-old couple with …

Understanding And Improving The System: The Effects Of Weighting On The Accuracy Of Political Polling In Arkansas, Beck Williams

Political Science Undergraduate Honors Theses

In an effort to increase the accuracy of statewide political polling in Arkansas, we explore the statistical strategy of weighting with a focus on one yearly opinion poll: The Arkansas Poll. We conduct over 70 weighting experiments on the 2016 and 2020 Arkansas Polls using a variety of variables and opinion questions. From these experiments, we find that while some weighted variables tend to create larger changes, weighting typically results in a single-digit percentage change that does not substantially shift or “flip” the majorities. Due to a greater rate of change through weighting in the 2020 Poll compared to the …

The Impact Of Social Controls And Vaccination On The Spread Of Covid-19 In New Jersey, Ariel J. Bonneau

Theses, Dissertations and Culminating Projects

The emergence of the novel coronavirus (SARS-CoV-2) in late 2019 has led to a global pandemic (COVID-19) which continues to cause enormous public health and economic challenges around the world. It is therefore important to improve our understanding of the outbreak and spread of COVID-19 as well as to investigate how one might contain or stop the spread of COVID-19 via different control measures. In this thesis, we consider a COVID-19 model based on an SEIR compartmental model. The model includes susceptible, vaccinated, exposed, pre-symptomatic, symptomatic infectious, asymptomatic infectious, hospitalized, recovered, and deceased compartments, each of which is sub-divided into …

Modeling The Dynamics Of Excitable Cells, Asja Alić

Theses, Dissertations and Culminating Projects

We consider an electrical parallel conductance membrane model which is an extension of the classical Hodgkin-Huxley neuronal model of excitability. This extended model describes the formation of the resting membrane potential and conductance, and the formation of action potentials in nodose A-type excitable cells. The model consists of a set of nonlinear ordinary differential equations which are numerically solved using the Python programming language. The results show that the model is capable of accurately describing experimental results including resting membrane potential and conductance, duration and form of action potentials, amplitude of the spike, oscillations, and activitydependent changes in [Ca^{2+ …}

Forecasting Electricity Load In New Jersey With Artificial Neural Networks, Erik W. Raab

Theses, Dissertations and Culminating Projects

Load forecasting is an important tool for both the energy and environmental sectors. It has progressed hand-in-hand with machine learning innovation, where recurrent neural networks, a type of artificial neural network, is primarily used. This thesis compares progressively complex, feed-forward artificial neural networks using a mix of weather and temporal data. We demonstrate that electrical load in New Jersey can be reliably predicted using memory-less algorithms with minimal predictors drawn from preexisting public data sources. The methods used in this thesis could be used to build competitive load forecasting models in other states, and if included in diverse model ensembles, …

Deep Depression Prediction On Longitudinal Data Via Joint Anomaly Ranking And Classification, Guansong Pang, Ngoc Thien Anh Pham, Emma Baker, Rebecca Bentley, Anton Van Den Hengel

Research Collection School Of Computing and Information Systems

A wide variety of methods have been developed for identifying depression, but they focus primarily on measuring the degree to which individuals are suffering from depression currently. In this work we explore the possibility of predicting future depression using machine learning applied to longitudinal socio-demographic data. In doing so we show that data such as housing status, and the details of the family environment, can provide cues for predicting future psychiatric disorders. To this end, we introduce a novel deep multi-task recurrent neural network to learn time-dependent depression cues. The depression prediction task is jointly optimized with two auxiliary anomaly …

Statistical Methods For Assessing Drug Interactions And Identifying Effect Modifiers Using Observational Data., Qian Xu

Electronic Theses and Dissertations

This dissertation consists of three projects related to causal inference based on observational data. In the first project, we propose a double robust to identify the effect modifiers and estimate optimal treatment. Observational studies differ from experimental studies in that assignment of subjects to treatments is not randomized but rather occurs due to natural mechanisms, which are usually hidden from the researchers. Many statistical methods to identify the treatment effect and select the optimal personalized treatment for experimental studies may not be suitable for observational studies any more. In this project, we propose a exible outcome model to select the …

Penalized Estimation Of Autocorrelation, Xiyan Tan

All Dissertations

This dissertation explored the idea of penalized method in estimating the autocorrelation (ACF) and partial autocorrelation (PACF) in order to solve the problem that the sample (partial) autocorrelation underestimates the magnitude of (partial) autocorrelation in stationary time series. Although finite sample bias corrections can be found under specific assumed models, no general formulae are available. We introduce a novel penalized M-estimator for (partial) autocorrelation, with the penalty pushing the estimator toward a target selected from the data. This both encapsulates and differs from previous attempts at penalized estimation for autocorrelation, which shrink the estimator toward the target value of zero. …