Statistics and Probability Commons^™

Assessing Extant Methods For Generating G-Optimal Designs And A Novel Methodology To Compute The G-Score Of A Candidate Design, Hyrum John Hansen

All Graduate Theses and Dissertations, Fall 2023 to Present

Experimental designs are used by scientists to allocate treatments such that statistical inference is appropriate. Most traditional experimental designs have mathematical properties that make them desirable under certain conditions. Optimal experimental designs are those where the researcher can exercise total control over the treatment levels to maximize a chosen mathematical property. As is common in literature, the experimental design is represented as a matrix where each column represents a variable, and each row represents a trial. We define a function that takes as input the design matrix and outputs its score. We then algorithmically adjust each entry until a design …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

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 …

Coarctation Duration And Severity Predict Risk Of Hypertension Precursors In A Preclinical Model And Hypertensive Status Among Patients, Arash Ghorbannia, Hilda Jurkiewicz, Lith Nasif, Abdillahi Ahmed, Jennifer Co-Vu, Mehdi Maadooliat, Ronald K. Woods, John F. Ladisa Jr.

Mathematical and Statistical Science Faculty Research and Publications

BACKGROUND:

Coarctation of the aorta (CoA) often leads to hypertension posttreatment. Evidence is lacking for the current >20 mm Hg peak-to-peak blood pressure (BP) gradient (BPGpp) guideline, which can cause aortic thickening, stiffening, and dysfunction. This study sought to find the BPGpp severity and duration that avoid persistent dysfunction in a preclinical model and test if predictors translate to hypertension status in patients with CoA.

METHODS:

Rabbits (n=75; 5–12/group) were exposed to mild, intermediate, or severe CoA (≤12, 13–19, ≥20 mm Hg BPGpp) for ≈1, 3, or 22 weeks using dissolvable and permanent sutures with thickening, stiffening, contraction, and endothelial …

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 …

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 …

On The Existence Of Periodic Traveling-Wave Solutions To Certain Systems Of Nonlinear, Dispersive Wave Equations, Jacob Daniels

All Graduate Theses and Dissertations, Fall 2023 to Present

A variety of physical phenomena can be modeled by systems of nonlinear, dispersive wave equations. Such examples include the propagation of a wave through a canal, deep ocean waves with small amplitude and long wavelength, and even the propagation of long-crested waves on the surface of lakes. An important task in the study of water wave equations is to determine whether a solution exists. This thesis aims to determine whether there exists solutions that both travel at a constant speed and are periodic for several systems of water wave equations. The work done in this thesis contributes to the subfields …

Ianova: Multi-Sample Means Comparisons For Imprecise Interval Data, Zachary Rios

All Graduate Theses and Dissertations, Fall 2023 to Present

In recent years, interval data has become an increasingly popular tool to solve modern data problems. Intervals are now often used for dimensionality reduction, data aggregation, privacy censorship, and quantifying awareness of various uncertainties. Among many statistical methods that are being studied and developed for interval data, the significance test is particularly of importance due to its fundamental value both in theory and practice. The difficulty in developing such tests mainly lies in the fact that the concept of normality does not extend naturally to interval data (due the range of an interval being necessarily non-negative), causing the exact tests …

Identifying Disease-Related Gene-Environment Interactions Based On Method Of Moments, Linchuan Shen

UNLV Theses, Dissertations, Professional Papers, and Capstones

Human diseases are often caused by a complex interplay of multiple factors, including genetics and environmental factors. These factors can play critical roles in the development and progression of diseases. Although genome-wide association studies (GWAS) have successfully identified many genetic variants associated with human diseases, the estimated effects of these variants are small and can explain only a relatively small portion of the heritability of the underlying diseases.

Detecting gene-environment interactions (G × E) can shed light on the biological mechanisms of diseases. However, most existing methods that investigate G × E only look at how one environmental …

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 …

Deterministic Global 3d Fractal Cloud Model For Synthetic Scene Generation, Aaron M. Schinder, Shannon R. Young, Bryan J. Steward, Michael L. Dexter, Andrew Kondrath, Stephen Hinton, Ricardo Davila

Faculty Publications

This paper describes the creation of a fast, deterministic, 3D fractal cloud renderer for the AFIT Sensor and Scene Emulation Tool (ASSET). The renderer generates 3D clouds by ray marching through a volume and sampling the level-set of a fractal function. The fractal function is distorted by a displacement map, which is generated using horizontal wind data from a Global Forecast System (GFS) weather file. The vertical windspeed and relative humidity are used to mask the creation of clouds to match realistic large-scale weather patterns over the Earth. Small-scale detail is provided by the fractal functions which are tuned to …

Trust In Human-Robot Collaborative Tasks, Jesse Parron

Theses, Dissertations and Culminating Projects

With recent advancements in machine learning and artificial intelligence, we have entered a new era of technology. With AI on the rise, it has led to many insurgencies in a variety of domains. One area that has particularly been growing at a rapid pace is robotics. Consequently, the ubiquitous rise of robots in new workspaces leads to the influx of human-robot collaboration. Human-robot collaboration is an important factor in this domain, as it mandates the relationship between humans and robots. One key component of this relationship is trust, which many people subconsciously determine when interacting with peers, strangers, and coworkers. …

Representation Learning For Generative Models With Applications To Healthcare, Astronautics, And Aviation, Van Minh Nguyen

Theses and Dissertations

This dissertation explores applications of representation learning and generative models to challenges in healthcare, astronautics, and aviation.

The first part investigates the use of Generative Adversarial Networks (GANs) to synthesize realistic electronic health record (EHR) data. An initial attempt at training a GAN on the MIMIC-IV dataset encountered stability and convergence issues, motivating a deeper study of 1-Lipschitz regularization techniques for Auxiliary Classifier GANs (AC-GANs). An extensive ablation study on the CIFAR-10 dataset found that Spectral Normalization is key for AC-GAN stability and performance, while Weight Clipping fails to converge without Spectral Normalization. Analysis of the training dynamics provided further …

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. …