Statistics and Probability Commons^™

Advancements In Gaussian Process Learning For Uncertainty Quantification, John C. Nicholson

All Dissertations

Gaussian processes are among the most useful tools in modeling continuous processes in machine learning and statistics. The research presented provides advancements in uncertainty quantification using Gaussian processes from two distinct perspectives. The first provides a more fundamental means of constructing Gaussian processes which take on arbitrary linear operator constraints in much more general framework than its predecessors, and the other from the perspective of calibration of state-aware parameters in computer models. If the value of a process is known at a finite collection of points, one may use Gaussian processes to construct a surface which interpolates these values to …

Groundwork For The Development Of Gpu Enabled Group Testing Regression Models, Paul Cubre

All Dissertations

In this dissertation, we develop novel techniques that allow for the regression analysis of data emerging from group testing processes and set the groundwork for graphic processing units (GPU) enabled implementations. Group testing primarily occurs in clinical laboratories, where it is used to quickly and cheaply diagnose patients. Typically, group testing tests a pooled specimen--several specimens combined into one sample--instead of testing individual specimens one-by-one. This method reduces costs by using fewer tests when the disease prevalence is low. Due to recent advances in diagnostic technology, group testing protocols were extended to incorporate multiplex assays, which are diagnostic tests that, …

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 …

Finding A Representative Distribution For The Tail Index Alpha, Α, For Stock Return Data From The New York Stock Exchange, Jett Burns

Electronic Theses and Dissertations

Statistical inference is a tool for creating models that can accurately display real-world events. Special importance is given to the financial methods that model risk and large price movements. A parameter that describes tail heaviness, and risk overall, is α. This research finds a representative distribution that models α. The absolute value of standardized stock returns from the Center for Research on Security Prices are used in this research. The inference is performed using R. Approximations for α are found using the ptsuite package. The GAMLSS package employs maximum likelihood estimation to estimate distribution parameters using the CRSP data. The …

Intraday Algorithmic Trading Using Momentum And Long Short-Term Memory Network Strategies, Andrew R. Whitinger Ii

Undergraduate Honors Theses

Intraday stock trading is an infamously difficult and risky strategy. Momentum and reversal strategies and long short-term memory (LSTM) neural networks have been shown to be effective for selecting stocks to buy and sell over time periods of multiple days. To explore whether these strategies can be effective for intraday trading, their implementations were simulated using intraday price data for stocks in the S&P 500 index, collected at 1-second intervals between February 11, 2021 and March 9, 2021 inclusive. The study tested 160 variations of momentum and reversal strategies for profitability in long, short, and market-neutral portfolios, totaling 480 portfolios. …

An Empirical Investigation Into The Impact Of Automated Grading, Alex James St. Aubin

UNLV Theses, Dissertations, Professional Papers, and Capstones

Context: Computer Science enrollment has seen increases in recent years. At the University of Nevada, Las Vegas we have seen an average year to year growth rate of 17.33% in the spring and 13.71% in the fall over the past 10 years in our entry level programming course. These enrollment increases have led to considerable additional costs for grading course material.Objective: The goal of this study is to determine the impact of automatic grading systems on students. If automatic grading is at least as effective as manual grading in practice, it may reduce cost under the context of at least …

Sensory Comparison Of Beer Carbonated Using Forced Carbonation And The Carbo Rock-It, Michala Smith

Biological and Agricultural Engineering Undergraduate Honors Theses

Craft brewing is a growing market which represents over 12% of beer produced in the United States. Dr. G Scott Osborn, PE invented the Carbo Rock-It™ to improve the carbonation process for craft breweries. The invention allows for shorter carbonation time and uses less CO₂, saving companies money and time. Because of the lack of gas losses through bubbling, Osborn theorized that the Carbo Rock-It could also prevent the “stripping of the nose” that can occur in traditional forced carbonation. Existing research supports the mechanism, as beer flavor and aroma volatiles have been detected during the release of …

The Effects Of Metronomic And Maximum-Tolerated Dose Chemotherapy In Colorectal Cancer Angiogenesis: A Combined Approach Using Endoscopic Diffuse Reflectance Spectroscopy And Mrna Expression, Ariel Isaac Mundo Ortiz

Graduate Theses and Dissertations

Colorectal cancer (CRC) continues to be one of the most incident and deadliest types of cancer worldwide. Chemotherapy has proven effective to reduce tumor burden for CRC patients, but there are several disadvantages associated with the use of mainstay maximtolerated dose (MTD) chemotherapeutic strategies. Metronomic chemotherapy (MET) has been developed as an alternative that addresses the shortcomings of maximum-tolerated dose chemotherapy but so far its effectiveness as a neoadjuvant strategy for CRC has not been explored.

This dissertation uses a combined optics and molecular biology approach (using diffuse reflectance spectroscopy and mRNA expression) to study the changes in angiogenesis and …

Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, Jennifer A. Reimers

Graduate Theses and Dissertations

Researchers have recognized that respondents may not answer items in a way that accurately reflects their attitude or trait level being measured. The resulting response data that deviates from what would be expected has been shown to have significant effects on the psychometric properties of a scale and analytical results. However, many studies that have investigated the detection of aberrant data and its effects have done so using dominance item response theory (IRT) models. It is unknown whether the impacts of aberrant data and the methodology used to identify aberrant responding when using dominance IRT models apply similarly when scales …

Dataset Evaluation For Data Trading Using Expected Loss And Homomorphic Encryption, Minsung Joo

Senior Honors Papers / Undergraduate Theses

Supervised machine learning suffers from the ``garbage-in garbage-out" phenomenon where the performance of a model is limited by the quality of the data. While a myriad of data is collected every second, there is no general rigorous method of evaluating the quality of a given dataset. This hinders fair pricing of data in scenarios where a buyer may look to buy data for use with machine learning. In this work, I propose using the expected loss corresponding to a dataset as a measure of its quality, relying on Bayesian methods for uncertainty quantification. Furthermore, I present a secure multi-party computation …

Posterior Predictive Model Checking Of The Hierarchical Rater Model, Nnamdi Chika Ezike

Graduate Theses and Dissertations

Fitting wrongly specified models to observed data may lead to invalid inferences about the model parameters of interest. The current study investigated the performance of the posterior predictive model checking (PPMC) approach in detecting model-data misfit of the hierarchical rater model (HRM). The HRM is a rater-mediated model that incorporates components of the polytomous item response theory (IRT) model, such as the partial credit model (PCM) and generalized partial credit model (GPCM), at the second level of the hierarchy, to model examinees’ responses to performance assessments. To date, the HRM has not been rigorously evaluated using PPMC techniques. Monte Carlo …

On Misuses Of The Kolmogorov–Smirnov Test For One-Sample Goodness-Of-Fit, Anthony Zeimbekakis

Honors Scholar Theses

The Kolmogorov–Smirnov (KS) test is one of the most popular goodness-of-fit tests for comparing a sample with a hypothesized parametric distribution. Nevertheless, it has often been misused. The standard one-sample KS test applies to independent, continuous data with a hypothesized distribution that is completely specified. It is not uncommon, however, to see in the literature that it was applied to dependent, discrete, or rounded data, with hypothesized distributions containing estimated parameters. For example, it has been "discovered" multiple times that the test is too conservative when the parameters are estimated. We demonstrate misuses of the one-sample KS test in three …

To Adopt Blockchain Technology Or Not: Is The Decision-Making Process Immune To Covid-19?, Rebecca Jauch

Thinking Matters Symposium

Blockchain technology has been shown to have advantages in improving the effectiveness of supply chain management. We use the Technology-Organization-Environment (TOE) framework with Threat-Rigidity Theory (TRT) to determine the factors that lead U.S. businesses to adopt blockchain technology, the factors that act as barriers to adoption, and the disruptive effect of COVID-19 on the rate of blockchain adoption

The Biggest Loser: How Tanking In Professional Sports Impacts Fan Perception, Julia Ayres

Honors Projects in Mathematics

Professional sports teams are adored nationwide for their talents and the pride they bring to their city for their efforts. However, not all teams take this responsibility seriously and will lose on purpose, or tank, to gain a higher draft pick in the future. Although the long-term goals of tanking are to help the organization, many people take issue with athletes not putting in their best efforts in every game. Teams in both the NBA and NFL are guilty of tanking to gain better draft picks but not all have found success in this process. This leads to important questions …

Analytical Study To Determine Significant Causes Of Increased No-Hitters In The 2021 Major League Baseball Season, Joel Robison

Honors Projects

Why were there so many no-hitters in the 2021 MLB season? This project focuses on possible significant causes to the record-breaking number of no-hitters pitched in the 2021 Major League Baseball season. Specifically, this project takes an analytical look at the recent trends in launch angles and spin rates to determine if there are any significant causes to the increased number of no-hitters in baseball. The random nature and unpredictability of the game of baseball make it almost impossible to come to any solid conclusions.

Examining Bacteriophage Reliance On The Tola Protein Of Escherichia Coli, Ellen Davis

Honors Projects

With the ever-growing crisis of antibiotic resistant bacteria, bacteriophage provide an important potential alternative therapy to treat infections when antibiotics are ineffective. For this reason, it is important to understand the mechanisms bacteriophage use to enter their bacterial host cell. The cellular envelope of the gram-negative bacterium E. coli contains a protein system known as Tol-Pal, which utilizes energy from the Proton Motive Force to carry out several cellular functions, including some associated with cellular division and outer-membrane stability. Certain bacteriophage take advantage of the energy produced by this system to cross the bacterial cell membrane. One important protein of …

A Novel Nonparametric Test For Heterogeneity Detection And Assessment Of Fluid Removal Among Crrt Patients In Icu, Shaowli Kabir

Theses and Dissertations--Epidemiology and Biostatistics

Over the past decade acute kidney injury (AKI) has been occurring among 20%-50% of patients admitted to the intensive care unit (ICU) in United States. Continuous renal replacement therapy (CRRT) has become a popular treatment method among these critically ill patients. But there are multiple complications in implementing this treatment, including discrepancies in practiced and prescribed fluid removal, possibly related to the heterogeneity among these patients. With mixture modeling there have been several techniques in detecting heterogeneity with their specific limitations. In this dissertation a novel nonparametric ‘d test’ will be used to detect heterogeneity among CRRT patients in ICU. …

An Educator’S Perspective Of The Tidyverse, Mine Çetinkaya-Rundel, Johanna Hardin, Benjamin Baumer, Amelia Mcnamara, Nicholas J. Horton, Colin W. Rundel

Statistical and Data Sciences: Faculty Publications

Computing makes up a large and growing component of data science and statistics courses. Many of those courses, especially when taught by faculty who are statisticians by training, teach R as the programming language. A number of instructors have opted to build much of their teaching around use of the tidyverse. The tidyverse, in the words of its developers, “is a collection of R packages that share a high-level design philosophy and low-level grammar and data structures, so that learning one package makes it easier to learn the next” (Wickham et al. 2019). These shared principles have led to the …

Efficient Low Dimensional Representation Of Vector Gaussian Distributions, Md Mahmudul Hasan

LSU Doctoral Dissertations

This dissertation seeks to find optimal graphical tree model for low dimensional representation of vector Gaussian distributions. For a special case we assumed that the population co-variance matrix $\Sigma_x$ has an additional latent graphical constraint, namely, a latent star topology. We have found the Constrained Minimum Determinant Factor Analysis (CMDFA) and Constrained Minimum Trace Factor Analysis (CMTFA) decompositions of this special $\Sigma_x$ in connection with the operational meanings of the respective solutions. Characterizing the CMDFA solution of special $\Sigma_x$, according to the second interpretation of Wyner's common information, is equivalent to solving the source coding problem of finding the minimum …

Vampires And Other Diseases: Stochastic Infection Dynamics Of Small Populations, Marijn Jaarsma

Honors Projects in Science and Technology

Mathematical models are powerful tools often applied in the field of epidemiology. The type and shape of the model will differ between different types of diseases. In this study, stochastic dynamical models are applied to the entertaining example of vampires, and the results of this analysis are compared to real-life diseases with small populations of infected. The data used comes from pop-culture depictions of vampires in literature, television shows, movies, and fan pages associated with these depictions. The aim of this study is to serve as an educational tool for modeling diseases with small populations to predict and control the …