Southern Methodist University
University of Connecticut - Storrs
Iowa State University
Istanbul Commerce University
Claremont McKenna College
University of Texas at Tyler
University of Nevada, Las Vegas
University of Tennessee, Knoxville
Assessing The Influence Of Health Policy And Population Mobility On Covid-19 Spread In Arkansas,
2022
University of Arkansas, Fayetteville
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 ...
Forecasting Razorback Baseball Game Outcomes,
2022
University of Arkansas, Fayetteville
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 ...
Analytical Study To Determine Significant Causes Of Increased No-Hitters In The 2021 Major League Baseball Season,
2022
Bowling Green State University
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.
Efficient Low Dimensional Representation Of Vector Gaussian Distributions,
2022
Louisiana State University and Agricultural and Mechanical College
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 ...
Statistical Applications To The Management Of Intensive Care And Step-Down Units,
2022
The University of Western Ontario
Statistical Applications To The Management Of Intensive Care And Step-Down Units, Yawo Mamoua Kobara
Electronic Thesis and Dissertation Repository
This thesis proposes three contributing manuscripts related to patient flow management, server decision-making, and ventilation time in the intensive care and step-down units system.
First, a Markov decision process (MDP) model with a Monte Carlo simulation was performed to compare two patient flow policies: prioritizing premature step-down and prioritizing rejection of patients when the intensive care unit is congested. The optimal decisions were obtained under the two strategies. The simulation results based on these optimal decisions show that a premature step-down strategy contributes to higher congestion downstream. Counter-intuitively, premature step-down should be discouraged, and patient rejection or divergence actions should ...
An Introduction To The Analysis Of Ranked Response Data,
2022
Ball State University
An Introduction To The Analysis Of Ranked Response Data, Holmes Finch
Practical Assessment, Research, and Evaluation
Researchers in many disciplines work with ranking data. This data type is unique in that it is often deterministic in nature (the ranks of items k-1 determine the rank of item k), and the difference in a pair of rank scores separated by k units is equivalent regardless of the actual values of the two ranks in the pair. Given its unique qualities, there are specific statistical analyses and models designed for use with ranking data. The purpose of this manuscript is to demonstrate a strategy for analyzing ranking data from sample description through the modeling of relative ranks ...
Simplified Ranking Model For The College Football Playoff Through Weighted Ranking,
2022
University of South Carolina - Columbia
Simplified Ranking Model For The College Football Playoff Through Weighted Ranking, Edward Buckhouse
Senior Theses
This research project looks to create a better system to rank college football teams in playoff contention. It uses surveys of coaches to create a weighted guideline to evaluate wins and losses for each team. This constructs a value for adjusted wins that is based on coaches’ data but strays away from the inherent bias that any single coach would have when ranking teams. The resulting Top 25 were then compared to the College Football Playoff final regular season rankings to gauge the success of the new system. The Adjusted Wins system that was established properly picked 13 out of ...
A Monte Carlo Analysis Of Seven Dichotomous Variable Confidence Interval Equations,
2022
Western Kentucky University
A Monte Carlo Analysis Of Seven Dichotomous Variable Confidence Interval Equations, Morgan Juanita Dubose
Masters Theses & Specialist Projects
Department of Psychological Sciences Western Kentucky University There are two options to estimate a range of likely values for the population mean of a continuous variable: one for when the population standard deviation is known and another for when the population standard deviation is unknown. There are seven proposed equations to calculate the confidence interval for the population mean of a dichotomous variable: normal approximation interval, Wilson interval, Jeffreys interval, Clopper-Pearson, Agresti-Coull, arcsine transformation, and logit transformation. In this study, I compared the percent effectiveness of each equation using a Monte Carlo analysis and the interval range over a range ...
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering,
2022
Channel Partners
Session 5: Equipment Finance Credit Risk Modeling - A Case Study In Creative Model Development & Nimble Data Engineering, Edward Krueger, Landon Thompson, Josh Moore
SDSU Data Science Symposium
This presentation will focus first on providing an overview of Channel and the Risk Analytics team that performed this case study. Given that context, we’ll then dive into our approach for building the modeling development data set, techniques and tools used to develop and implement the model into a production environment, and some of the challenges faced upon launch. Then, the presentation will pivot to the data engineering pipeline. During this portion, we will explore the application process and what happens to the data we collect. This will include how we extract & store the data along with how it ...
Applying Machine Learning Algorithms For Face Mask Detections,
2022
The University of Akron
Applying Machine Learning Algorithms For Face Mask Detections, Mackenzie Frato
Williams Honors College, Honors Research Projects
Goal: Apply multiple machine learning techniques to Face Mask images to detect if a student is wear a Face Mask and/or wearing it incorrectly or not at all. Methodology: Use 2-3 different machine learning techniques to develop this program. Will choose these techniques as I research over the semester. The best technique will be the final one used, but many will be explored. Validation techniques will be used to see which is the best technique. Timeline: Choose Dataset - October 1st, Choose techniques - October 31st, Research techniques/validation - November 31st, Begin writing code - December 13th, Finish code - February 1st, Finish ...
Estimating The Statistics Of Operational Loss Through The Analyzation Of A Time Series,
2022
Virginia Commonwealth University
Estimating The Statistics Of Operational Loss Through The Analyzation Of A Time Series, Maurice L. Brown, Cheng Ly
Theses and Dissertations
In the world of finance, appropriately understanding risk is key to success or failure because it is a fundamental driver for institutional behavior. Here we focus on risk as it relates to the operations of financial institutions, namely operational risk. Quantifying operational risk begins with data in the form of a time series of realized losses, which can occur for a number of reasons, can vary over different time intervals, and can pose a challenge that is exacerbated by having to account for both frequency and severity of losses. We introduce a stochastic point process model for the frequency distribution ...
Understanding The Comparative Fit Index: It's All About The Base!,
2021
CEMO: Centre for Educational Measurement at the university of Oslo
Understanding The Comparative Fit Index: It's All About The Base!, Saskia Van Laar, Johan Braeken
Practical Assessment, Research, and Evaluation
Despite the sensitivity of fit indices to various model and data characteristics in structural equation modeling, these fit indices are used in a rigid binary fashion as a mere rule of thumb threshold value in a search for model adequacy. Here, we address the behavior and interpretation of the popular Comparative Fit Index (CFI) by stressing that its metric for model assessment is the amount of misspecification in a baseline model and by further decomposition into its fundamental components: sample size, number of variables and the degree of multivariate dependence in the data. Simulation results show how these components influence ...
Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods,
2021
Southern Methodist University
Identification And Characterization Of Forest Fire Risk Zones Leveraging Machine Learning Methods, Joshua Balson, Matt Chinchilla, Cam Lu, Jeff Washburn, Nibhrat Lohia
SMU Data Science Review
Across the United States, record numbers of wildfires are observed costing billions of dollars in property damage, polluting the environment, and putting lives at risk. The ability of emergency management professionals, city planners, and private entities such as insurance companies to determine if an area is at higher risk of a fire breaking out has never been greater. This paper proposes a novel methodology for identifying and characterizing zones with increased risks of forest fires. Methods involving machine learning techniques use the widely available and recorded data, thus making it possible to implement the tool quickly.
Confidence Interval For The Mean Of A Beta Distribution,
2021
Stephen F Austin State University
Confidence Interval For The Mean Of A Beta Distribution, Sean Rangel
Electronic Theses and Dissertations
Statistical inference for the mean of a beta distribution has become increasingly popular in various fields of academic research. In this study, we developed a novel statistical model from likelihood-based techniques to evaluate various confidence interval techniques for the mean of a beta distribution. Simulation studies will be implemented to compare the performance of the confidence intervals. In addition to the development and study involving confidence intervals, we will also apply the confidence intervals to real biological data that was gathered by the Department of Biology at Stephen F. Austin State University and provide recommendations on the best practice.
Interpolating Missing Data And Comparing Performance Of Common Interpolation Techniques From A 30-Year Water Quality Dataset,
2021
University of Wisconsin - La Crosse
Interpolating Missing Data And Comparing Performance Of Common Interpolation Techniques From A 30-Year Water Quality Dataset, Wako Bungula, Danelle M. Larson Dr., Killian Davis, Richard Erickson Dr., Amber Lee, Casey Mckean, Frederick Miller, Alaina Stockdill, Enrika Hlavacek
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Estimation Analysis For The Seir Model With Stochastic Perturbation For The Covid-19 Outbreak In Bogotá,
2021
Universidad Nacional de Colombia
Estimation Analysis For The Seir Model With Stochastic Perturbation For The Covid-19 Outbreak In Bogotá, Viswanathan Arunachalam, Andres Rios-Gutierrez
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Species Abundance Distributions And The Canon Of Classical Music,
2021
University of Utah
Species Abundance Distributions And The Canon Of Classical Music, Noelle Atkin
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Statistical Modeling Of Sars-Cov-2 Mutation In The U.S.,
2021
University of St. Francis
Statistical Modeling Of Sars-Cov-2 Mutation In The U.S., Yuru Jing, Angela Antonou
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Statistical Improvements For Ecological Learning About Spatial Processes,
2021
University of Massachusetts Amherst
Statistical Improvements For Ecological Learning About Spatial Processes, Gaetan L. Dupont
Masters Theses
Ecological inquiry is rooted fundamentally in understanding population abundance, both to develop theory and improve conservation outcomes. Despite this importance, estimating abundance is difficult due to the imperfect detection of individuals in a sample population. Further, accounting for space can provide more biologically realistic inference, shifting the focus from abundance to density and encouraging the exploration of spatial processes. To address these challenges, Spatial Capture-Recapture (“SCR”) has emerged as the most prominent method for estimating density reliably. The SCR model is conceptually straightforward: it combines a spatial model of detection with a point process model of the spatial distribution of ...
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities,
2021
University of Massachusetts Amherst
Monitoring Mammals At Multiple Scales: Case Studies From Carnivore Communities, Kadambari Devarajan
Doctoral Dissertations
Carnivores are distributed widely and threatened by habitat loss, poaching, climate change, and disease. They are considered integral to ecosystem function through their direct and indirect interactions with species at different trophic levels. Given the importance of carnivores, it is of high conservation priority to understand the processes driving carnivore assemblages in different systems. It is thus essential to determine the abiotic and biotic drivers of carnivore community composition at different spatial scales and address the following questions: (i) What factors influence carnivore community composition and diversity? (ii) How do the factors influencing carnivore communities vary across spatial and temporal ...
