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Articles 1 - 30 of 79
Full-Text Articles in Physical Sciences and Mathematics
Comparing North American Professional Sports League Season Formats Using Monte Carlo Simulation, Lathan Gregg
Comparing North American Professional Sports League Season Formats Using Monte Carlo Simulation, Lathan Gregg
Industrial Engineering Undergraduate Honors Theses
Each NFL, NBA, and MLB season consists of a regular season, in which teams play a set number of scheduled games and a playoff, in which qualifying teams compete for a championship. At the conclusion of each season, teams are ranked based on their performance throughout the season. This study aims to investigate the ability of each league's season format to accurately rank teams using Monte Carlo simulation. Matches between two teams are simulated by using the team’s assigned strength ranks to calculate a winning probability for each team. The winning probabilities are simulated with different skill values, dictating how …
The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi
The Quantitative Analysis And Visualization Of Nfl Passing Routes, Sandeep Chitturi
Computer Science and Computer Engineering Undergraduate Honors Theses
The strategic planning of offensive passing plays in the NFL incorporates numerous variables, including defensive coverages, player positioning, historical data, etc. This project develops an application using an analytical framework and an interactive model to simulate and visualize an NFL offense's passing strategy under varying conditions. Using R-programming and data management, the model dynamically represents potential passing routes in response to different defensive schemes. The system architecture integrates data from historical NFL league years to generate quantified route scores through designed mathematical equations. This allows for the prediction of potential passing routes for offensive skill players in response to the …
Analyses Of Effect Indices Across Single-Case Research Designs In Counseling, Cian L. Brown
Analyses Of Effect Indices Across Single-Case Research Designs In Counseling, Cian L. Brown
Graduate Theses and Dissertations
Single case research design (SCRD) is a common methodology used across clinical disciplines to determine treatments effectiveness by comparing treatment conditions to baseline conditions in individual cases, usually among researchers working with smaller samples. Although popular within behavioral disciplines such as special education and behavioral analysis, studies have begun to emerge in counseling. However, guidance and current understanding of the use of SCRD in counseling is limited. A content analysis of counseling journals from 2003 to 2014 yielded only 7 studies using SCRD. In 2015, the flagship counseling journal, Journal of Counseling and Development, published a special issue on the …
Bayesian Learning Of Spatiotemporal Source Distribution For Beached Microplastic In The Gulf Of Mexico, David Pojunas
Bayesian Learning Of Spatiotemporal Source Distribution For Beached Microplastic In The Gulf Of Mexico, David Pojunas
Graduate Theses and Dissertations
Over the last several decades, plastic waste has gradually accumulated while slowly degrading in terrestrial and oceanic environments. Recently, there has been an increased effort to identify the possible sources of plastic to understand how they affect vulnerable beaches. This issue is of particular concern in the Gulf of Mexico due to the presence of oil, natural gas, and plastic production. In this thesis, we expand upon existing Bayesian plastic attribution models and develop a rigorous statistical framework to map observed beached microplastics to their sources. Within this framework, we combine Lagrangian backtracking simulations of floating particles using nurdle beaching …
Comparative Analysis Of Teacher Effects Parameters In Models Used For Assessing School Effectiveness: Value-Added Models & Persistence, Merlin J. Kamgue
Comparative Analysis Of Teacher Effects Parameters In Models Used For Assessing School Effectiveness: Value-Added Models & Persistence, Merlin J. Kamgue
Graduate Theses and Dissertations
Longitudinal measures for students have become increasingly popular to estimate the effects of individual teachers and schools. Value-added models are one of the approaches using longitudinal data to evaluate teachers and schools. In the value-added model (VAM) literature, many statistical approaches have been developed and used to estimate teacher or school effects on student learning. This study opted to use a Bayesian multivariate model for evaluating teacher effects. The generalized persistence models can handle longitudinal data, not vertically scaled, allowing for a below-par teacher’s effects correlation across test administrations. This study first generated longitudinal students’ test score data and used …
A Comparative Study Of Techniques For Non-Monotonic Dependence With Emphasis On Sensitivity To Sample Size, Noise Level And Computational Attributes, Fariha Tasnim
Graduate Theses and Dissertations
Evaluating association between variables is often of interest by many researchers. To serve this purpose, different association measures have been developed. However, type of relation between variables affects the degree of relationship. Hence, detection of the rela- tionship between variables is germane to measuring the correlation coefficient. With that mindset, here we explored six non-monotonic measure of association techniques and com- pared them with three classical approaches. Due to inconsistency in definition and range of different techniques, it is not feasible to compare the correlation estimates as their nature of variability differ. Therefore, we used permutation test based on Monte …
Comparing Predictive Performance Of Garch And Stochastic Volatility Models, Swapnaneel Nath
Comparing Predictive Performance Of Garch And Stochastic Volatility Models, Swapnaneel Nath
Graduate Theses and Dissertations
This paper compares the predictive performance of two commonly used financial models, the Generalized Auto-Regressive Conditional Heteroskedasticity (GARCH) model, and the Stochastic Volatility model. Both techniques are used in the finance literature to model returns on an asset; the main difference between the two is that the former holds volatility as deterministic, whereas the latter treats it as a stochastic component. Three 10-year periods (2006-15, 2008-17, and 2010-19) of returns of the S&P-500 Index are used to train the two models. The parameter estimation is done using Hamiltonian Monte Carlo. Then, using Sequential Monte Carlo updates, returns for 2016, 2018, …
Effects Of Land Use On Soil Microbial Communities In Tropical Montane Forests Of Malaysian Borneo, Yang Kai Tang
Effects Of Land Use On Soil Microbial Communities In Tropical Montane Forests Of Malaysian Borneo, Yang Kai Tang
Graduate Theses and Dissertations
Land use, such as logging and forest conversion to agriculture, can modify soil physicochemical and biological properties, and affect soil health. To understand how land use change can impact soil properties and canopy structure, we used a land use gradient in Malaysian Borneo consisting of six sites, including old growth forests, mixed forests, and agriculture fields. Specifically, we aimed to answer the following questions: (1) How do soil physicochemical properties vary across land use types? (2) Does bacterial diversity and composition vary across different land use types? (3) Does fungal diversity and composition vary across different land use types? We …
Mle And Eap Methods For Estimating Ability Scores For Data Of Varying Sample Size And Item Length, Sahar Taji
Mle And Eap Methods For Estimating Ability Scores For Data Of Varying Sample Size And Item Length, Sahar Taji
Graduate Theses and Dissertations
In this research, the performance of two popular estimators, Maximum Likelihood Estimator(MLE) and Bayesian Expected a Posteriori (EAP) is studied and compared in estimating the latent ability score in an Item Response Theory (IRT) model. The 2-Parameter Logistic (2PL) IRT model which is characterized by difficulty and discrimination item parameters is used to estimate the latent ability scores. Several datasets are generated for variety of sample size and item length values. The Monte-Carlo simulation is used to analyze the performance of the estimators. Results show that MLE produces reliable results with low root mean square error (RMSE) across all datasets. …
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Ensemble Tree-Based Machine Learning For Imaging Data, Reza Iranzad
Graduate Theses and Dissertations
In particular medical imaging data, such as positron emission tomography (PET), computed tomography (CT), and fluorescence intravital microscopy (IVM), have become prevalent for use in a wide variety of applications, from diagnostic purposes, tracking diseases' progress, and monitoring the effectiveness of treatments to decision-making processes. The detailed information generated by medical imaging has enabled physicians to provide more comprehensive care. Although numerous machine learning algorithms, especially those used for imaging data, have been developed, dealing with unique structures in imaging data remained a big challenge. In this dissertation, we are proposing novel statistical tree-based methods with more efficient and more …
Improving Computation For Hierarchical Bayesian Spatial Gaussian Mixture Models With Application To The Analysis Of Thz Image Of Breast Tumor, Jean Remy Habimana
Improving Computation For Hierarchical Bayesian Spatial Gaussian Mixture Models With Application To The Analysis Of Thz Image Of Breast Tumor, Jean Remy Habimana
Graduate Theses and Dissertations
In the first chapter of this dissertation we give a brief introduction to Markov chain Monte Carlo methods (MCMC) and their application in Bayesian inference. In particular, we discuss the Metropolis-Hastings and conjugate Gibbs algorithms and explore the computational underpinnings of these methods. The second chapter discusses how to incorporate spatial autocorrelation in linear a regression model with an emphasis on the computational framework for estimating the spatial correlation patterns.
The third chapter starts with an overview of Gaussian mixture models (GMMs). However, because in the GMM framework the observations are assumed to be independent, GMMs are less effective when …
Hiding In Plain Sight: Accounting For Rate Heterogeneity In Trait Evolution Models, James Boyko
Hiding In Plain Sight: Accounting For Rate Heterogeneity In Trait Evolution Models, James Boyko
Graduate Theses and Dissertations
Within the last four decades, phylogenetic comparative methods have become the defacto method of analysis for comparative biologists. The availability of high-quality comparative datasets has been matched by an explosion of possible phylogenetic models. In large part, the efforts to increase the realism of phylogenetic comparative methods has been successful as evidenced by their widespread use. To this extensive literature, my contributions are modest. I have focused my dissertation work on two main themes. First, most phenotypic evolution is not independent of other phenotypes. Changes in a particular character may influence changes in another and modeling these characters in isolation …
How Blockchain Solutions Enable Better Decision Making Through Blockchain Analytics, Sammy Ter Haar
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. …
Forecasting Razorback Baseball Game Outcomes, Austin Raabe
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 …
Aberrant Responding With Underlying Dominance And Unfolding Response Processes: Examining Model Fit And Performance Of Person-Fit Statistics, Jennifer A. Reimers
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 …
Posterior Predictive Model Checking Of The Hierarchical Rater Model, Nnamdi Chika Ezike
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 …
Sensory Comparison Of Beer Carbonated Using Forced Carbonation And The Carbo Rock-It, Michala Smith
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 …
Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas, Tayden Barretto
Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas, Tayden Barretto
Industrial Engineering Undergraduate Honors Theses
The outbreak of COVID-19 has created a major crisis across the world since its start in 2019, and its influence on every realm of society is undeniable. Globally, more than 500 million cases have been recorded since March 2020, with almost 6 million deaths. In the wake of this crisis, many governments and health organizations have taken steps and precautions to mitigate its spread. These steps involve public mandates of information, reducing frequency of personal contact, and use of masks to minimize the risk of transmission. Current access to mobility data released from Google detailing population movements has provided a …
Multi-Trophic Biodiversity Increases With Increasing Structural Complexity Of Forest Canopy, Ayanna St. Rose
Multi-Trophic Biodiversity Increases With Increasing Structural Complexity Of Forest Canopy, Ayanna St. Rose
Graduate Theses and Dissertations
Understanding the effects of forest canopy structural complexity on multi-trophic diversity is critical for conserving biodiversity and managing land sustainably. But multi-trophic diversity is often ignored when making decisions about land management due to lack of cost- and time-effective methods to evaluate it. Here, we explored a new method based on widely available remote sensing data to quantify canopy structural complexity and its relationships with multi-trophic biodiversity at landscape scale using 32 forested sites of the National Ecological Observatory Network. We investigated the influence of vertical and horizontal structural complexity of forest canopy on multi-trophic (primary producers, herbivores (beetles), omnivores …
Understanding And Improving The System: The Effects Of Weighting On The Accuracy Of Political Polling In Arkansas, Beck Williams
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 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
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 …
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Statistical Modeling, Learning And Computing For Stochastic Dynamics Of Complex Systems, Mohammadmahdi Hajiha
Graduate Theses and Dissertations
With the recent advances in sensor technology, it is much easier to collect and store streams of system operational and environmental (SOE) data. These data can be used as input to model the underlying behavior of complex engineered systems and phenomenons if appropriate algorithms with well-defined assumptions are developed. This dissertation is comprised of the research work to show the applicability of SOE data when fed into proposed tailored algorithms. The first purposes of these algorithms are to estimate and analyze the reliability of a system as elaborated in Chapter 2. This chapter provides the derivation of closed-form expressions that …
Data-Driven Statin Initiation Evaluation And Optimization For Prediabetes Population, Muhenned A. Abdulsahib
Data-Driven Statin Initiation Evaluation And Optimization For Prediabetes Population, Muhenned A. Abdulsahib
Graduate Theses and Dissertations
This dissertation develops quantitative models to support medical decision making of statininitiation considering the uncertainty in disease progression for prediabetes patients. A mathematical model is built to help medical decision-makers take action of statin initiation under uncertainty in future prediabetes progressions. The association between cholesterol drug use, such as statin, and elevating glucose level attracted considerable amounts of attention in the literature. Statin effects on glucose vary with respect to different levels of glucose. The first chapter of this dissertation introduces the problem and an overview of the tools that will be used to solve it. In the second chapter …
Knowledge Discovery From Complex Event Time Data With Covariates, Samira Karimi
Knowledge Discovery From Complex Event Time Data With Covariates, Samira Karimi
Graduate Theses and Dissertations
In particular engineering applications, such as reliability engineering, complex types of data are encountered which require novel methods of statistical analysis. Handling covariates properly while managing the missing values is a challenging task. These type of issues happen frequently in reliability data analysis. Specifically, accelerated life testing (ALT) data are usually conducted by exposing test units of a product to severer-than-normal conditions to expedite the failure process. The resulting lifetime and/or censoring data are often modeled by a probability distribution along with a life-stress relationship. However, if the probability distribution and life-stress relationship selected cannot adequately describe the underlying failure …
Statistical Modeling For High-Dimensional Compositional Data With Applications To The Human Microbiome, Thy Dao
Graduate Theses and Dissertations
Compositional data refer to the data that lie on a simplex, which are common in many scientific domains such as genomics, geology, and economics. As the components in a composition must sum to one, traditional tests based on unconstrained data become inappropriate, and new statistical methods are needed to analyze this special type of data. This dissertation is motivated by some statistical problems arising in the analysis of compositional data. In particular, we focus on the high-dimensional and over-dispersed setting, where the dimensionality of compositions is greater than the sample size and the dispersion parameter is moderate or large. In …
Evaluating The Efficiency Of Markov Chain Monte Carlo Algorithms, Thuy Scanlon
Evaluating The Efficiency Of Markov Chain Monte Carlo Algorithms, Thuy Scanlon
Graduate Theses and Dissertations
Markov chain Monte Carlo (MCMC) is a simulation technique that produces a Markov chain designed to converge to a stationary distribution. In Bayesian statistics, MCMC is used to obtain samples from a posterior distribution for inference. To ensure the accuracy of estimates using MCMC samples, the convergence to the stationary distribution of an MCMC algorithm has to be checked. As computation time is a resource, optimizing the efficiency of an MCMC algorithm in terms of effective sample size (ESS) per time unit is an important goal for statisticians. In this paper, we use simulation studies to demonstrate how the Gibbs …
Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao
Privacy-Preserving Cloud-Assisted Data Analytics, Wei Bao
Graduate Theses and Dissertations
Nowadays industries are collecting a massive and exponentially growing amount of data that can be utilized to extract useful insights for improving various aspects of our life. Data analytics (e.g., via the use of machine learning) has been extensively applied to make important decisions in various real world applications. However, it is challenging for resource-limited clients to analyze their data in an efficient way when its scale is large. Additionally, the data resources are increasingly distributed among different owners. Nonetheless, users' data may contain private information that needs to be protected.
Cloud computing has become more and more popular in …
Applying Emotional Analysis For Automated Content Moderation, John Shelnutt
Applying Emotional Analysis For Automated Content Moderation, John Shelnutt
Computer Science and Computer Engineering Undergraduate Honors Theses
The purpose of this project is to explore the effectiveness of emotional analysis as a means to automatically moderate content or flag content for manual moderation in order to reduce the workload of human moderators in moderating toxic content online. In this context, toxic content is defined as content that features excessive negativity, rudeness, or malice. This often features offensive language or slurs. The work involved in this project included creating a simple website that imitates a social media or forum with a feed of user submitted text posts, implementing an emotional analysis algorithm from a word emotions dataset, designing …
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri
Improving Bayesian Graph Convolutional Networks Using Markov Chain Monte Carlo Graph Sampling, Aneesh Komanduri
Computer Science and Computer Engineering Undergraduate Honors Theses
In the modern age of social media and networks, graph representations of real-world phenomena have become incredibly crucial. Often, we are interested in understanding how entities in a graph are interconnected. Graph Neural Networks (GNNs) have proven to be a very useful tool in a variety of graph learning tasks including node classification, link prediction, and edge classification. However, in most of these tasks, the graph data we are working with may be noisy and may contain spurious edges. That is, there is a lot of uncertainty associated with the underlying graph structure. Recent approaches to modeling uncertainty have been …
Cointegration And Statistical Arbitrage Of Precious Metals, Judge Van Horn
Cointegration And Statistical Arbitrage Of Precious Metals, Judge Van Horn
Finance Undergraduate Honors Theses
When talking about financial instruments correlation is often thrown around as a measure of the relation between two securities. An often more useful or tradeable measure is cointegration. Cointegration is the measure of two securities tendency to revert to an average price over time. In other words, cointegration ignores directionality and only cares about the distance between two securities. For a mean reversion strategy such as statistical arbitrage cointegration proves to be a far more reliable statistical measure of mean reversion, and while it is more reliable than correlation it still has its own problems. One thing to consider is …