Statistics and Probability Commons^™

Improving Data-Driven Infrastructure Degradation Forecast Skill With Stepwise Asset Condition Prediction Models, Kurt R. Lamm, Justin D. Delorit, Michael N. Grussing, Steven J. Schuldt

Faculty Publications

Organizations with large facility and infrastructure portfolios have used asset management databases for over ten years to collect and standardize asset condition data. Decision makers use these data to predict asset degradation and expected service life, enabling prioritized maintenance, repair, and renovation actions that reduce asset life-cycle costs and achieve organizational objectives. However, these asset condition forecasts are calculated using standardized, self-correcting distribution models that rely on poorly-fit, continuous functions. This research presents four stepwise asset condition forecast models that utilize historical asset inspection data to improve prediction accuracy: (1) Slope, (2) Weighted Slope, (3) Condition-Intelligent Weighted Slope, and (4) …

Between “Breaking” And “Building”: The Bridge Theory Of Research Evaluation, Fang Xu, Xiaoxuan Li

Bulletin of Chinese Academy of Sciences (Chinese Version)

How to build "new standards" after breaking "Siwei" is a hot and difficult issue in the current reform of research evaluation, which urgently needs good theoretical and methodological support. In this context, this study puts forward the BRIDGE theory of research evaluation of scientific researchers' achievements, which is to integrate the reasonable elements in the quantitative evaluation based on SCI papers into the "new standard" based on peer review, so as to build a bridge between quantitative analysis and qualitative evaluation. The practical application of BRIDGE theory is expressed as "Six Steps", in which the second step "Recode" and the …

Release Of Vocs, Gasses, And Bacteria From Contaminated Landings And Creeks Of Ogeechee River Basin, Victoria A. Clower, Melanie Sparrow, Atin Adhikari

Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications

River landings are common public grounds, visited by many people every day. The aftermath of visiting these places may be unsettling since much trash is left behind and scattered throughout. The litter collects and with each rain or high wind, it has a better chance of ending up in our streams, rivers, creeks, and eventually our oceans. The main purpose of this study was to measure both air and water quality throughout the Ogeechee River basin in South Georgia to determine how each was impacted by trash. Ammonia, methane, and volatile organic compounds (VOCs) along with temperature and humidity were …

Mathematical Models Yield Insights Into Cnns: Applications In Natural Image Restoration And Population Genetics, Ryan Cecil

Electronic Theses and Dissertations

Due to a rise in computational power, machine learning (ML) methods have become the state-of-the-art in a variety of fields. Known to be black-box approaches, however, these methods are oftentimes not well understood. In this work, we utilize our understanding of model-based approaches to derive insights into Convolutional Neural Networks (CNNs). In the field of Natural Image Restoration, we focus on the image denoising problem. Recent work have demonstrated the potential of mathematically motivated CNN architectures that learn both `geometric' and nonlinear higher order features and corresponding regularizers. We extend this work by showing that not only can geometric features …

The Association Between Dietary Inflammatory Index, Dietary Antioxidant Index, And Mental Health In Adolescent Girls: An Analytical Study, Parvin Dehghan, Marzieh Nejati, Amir Almasi-Hashiani, Sevda Saleh-Ghadimi, Rezza Parsi, Hamed Jafari-Vayghan, Nitin Shivappa Mbbs, Mph, Ph.D., James Hébert Scd

Faculty Publications

Background Diet is considered as one of the modifiable factors that appears to exert a vital role in psychological status. In this way, we designed this study to examine the association between dietary inflammatory index (DII), dietary antioxidant index (DAI), and mental health in female adolescents. Methods This cross-sectional study included 364 female adolescents selected from high schools in the five regions of Tabriz, Iran. A 3-day food record was used to extract the dietary data and calculate DII/DAI scores. DII and DAI were estimated to assess the odds of depression, anxiety, and stress based on the Depression Anxiety Stress …

Defining Viable Solar Resource Locations In The Southeast United States Using The Satellite-Based Glass Product, Jolie Kavanagh

Theses and Dissertations

This research uses satellite data and the moment statistics to determine if solar farms can be placed in the Southeast US. From 2001-2019, the data are analyzed in reference to the Southwest US, where solar farms are located. The clean energy need is becoming more common; therefore, more locations than arid environments must be observed. The Southeast US is the main location of interest due to the warm, moist environment throughout the year. This research uses the Global Land Surface Satellite (GLASS) photosynthetically active radiation product (PAR) to determine viable locations for solar panels. A probability density function (PDF) along …

Exploration In Mental Performance For Division 1 Sec College Football Student Athletes, Alex Burgdorf

Department of Occupational Therapy Entry-Level Capstone Projects

The stigma surrounding mental health in sports has made intervention difficult. “There is a need for various actors to provide more effective strategies to overcome the stigma that surrounds mental illness, increase mental health literacy in the athlete/coach community, and address athlete-specific barriers to seeking treatment for mental illness” (Castadelli-Maia et.al 2019). The athletes in the football program at the University of Tennessee face more pressure today than ever in history. They have their class schedule, practice and training every day, and meetings with their position coaches. Now, with the introduction of name, image, and likeness (NIL) allowing players to …

Comparative Antiplatelet Effects Of Chlorthalidone And Hydrochlorothiazide, Khalid Bashir, Tammy Burns, Samuel J. Pirruccello, Sarah J. Aurit

Department of Statistics: Faculty Publications

Chlorthalidone (CTD) may be superior to hydrochlorothiazide (HCTZ) in the reduction of adverse cardiovascular events in hypertensive patients. The mechanism of the potential benefit of CTD could be related to antiplatelet effects. The objective of this study was to determine if CTD or HCTZ have antiplatelet effects. This study was a prospective, double-blind, randomized, three-way crossover comparison evaluating the antiplatelet effects of CTD, HCTZ, and aspirin (ASA) in healthy volunteers. The effects of these treatments on platelet activation and aggregation were assessed using a well-established method with five standard platelet agonists. Thirty-four patients completed the three-way crossover comparing pre- and …

Debiasing Cyber Incidents – Correcting For Reporting Delays And Under-Reporting, Seema Sangari

Doctor of Data Science and Analytics Dissertations

This research addresses two key problems in the cyber insurance industry – reporting delays and under-reporting of cyber incidents. Both problems are important to understand the true picture of cyber incident rates. While reporting delays addresses the problem of delays in reporting due to delays in timely detection, under-reporting addresses the problem of cyber incidents frequently under-reported due to brand damage, reputation risk and eventual financial impacts.

The problem of reporting delays in cyber incidents is resolved by generating the distribution of reporting delays and fitting modeled parametric distributions on the given domain. The reporting delay distribution was found to …

Abm Simulation Model Of A Pandemic For Optimizing Vaccination Strategy, Gibeom Park

Theses and Dissertations

This study presents a process-oriented hybrid model for individuals' immune responses and interactions involving vaccination to describe the trend of contagious disease and estimate the future societal cost. The model considers "recovery" as a non-absorbing state and incorporates various infection stage states including two symptomatic states. To model contagiousness to be consistent with the current pandemic and include that the spread of a disease depends on the mobility of people, we developed an Agent-Based Simulator that fitted to the particular model used in this study and can test various what-if scenarios. We improved the simulator considerably by appying data structures …

Dynamic Prediction For Alternating Recurrent Events Using A Semiparametric Joint Frailty Model, Jaehyeon Yun

Statistical Science Theses and Dissertations

Alternating recurrent events data arise commonly in health research; examples include hospital admissions and discharges of diabetes patients; exacerbations and remissions of chronic bronchitis; and quitting and restarting smoking. Recent work has involved formulating and estimating joint models for the recurrent event times considering non-negligible event durations. However, prediction models for transition between recurrent events are lacking. We consider the development and evaluation of methods for predicting future events within these models. Specifically, we propose a tool for dynamically predicting transition between alternating recurrent events in real time. Under a flexible joint frailty model, we derive the predictive probability of …

A Positivity Preserving, Energy Stable Finite Difference Scheme For The Flory-Huggins-Cahn-Hilliard-Navier-Stokes System, Wenbin Chen, Jianyu Jing, Cheng Wang, Xiaoming Wang

Mathematics and Statistics Faculty Research & Creative Works

In this paper, we propose and analyze a finite difference numerical scheme for the Cahn-Hilliard-Navier-Stokes system, with logarithmic Flory-Huggins energy potential. in the numerical approximation to the singular chemical potential, the logarithmic term and the surface diffusion term are implicitly updated, while an explicit computation is applied to the concave expansive term. Moreover, the convective term in the phase field evolutionary equation is approximated in a semi-implicit manner. Similarly, the fluid momentum equation is computed by a semi-implicit algorithm: implicit treatment for the kinematic diffusion term, explicit update for the pressure gradient, combined with semi-implicit approximations to the fluid convection …

Sex And Gender Differences In Symptoms Of Early Psychosis: A Systematic Review And Meta-Analysis, Brooke Carter, Jared Wootten, Suzanne Archie, Amanda L Terry, Kelly K. Anderson

Epidemiology and Biostatistics Publications

First-episode psychosis (FEP) can be quite variable in clinical presentation, and both sex and gender may account for some of this variability. Prior literature on sex or gender differences in symptoms of psychosis have been inconclusive, and a comprehensive summary of evidence on the early course of illness is lacking. The objective of this study was to conduct a systematic review and meta-analysis of the literature to summarize prior evidence on the sex and gender differences in the symptoms of early psychosis. We conducted an electronic database search (MEDLINE, Scopus, PsycINFO, and CINAHL) from 1990 to present to identify quantitative …

Advanced High Dimensional Regression Techniques, Yuan Yang

All Dissertations

This dissertation focuses on developing high dimensional regression techniques to analyze large scale data using both Bayesian and frequentist approaches, motivated by data sets from various disciplines, such as public health and genetics. More specifically, Chapters 2 and Chapter 4 take a Bayesian approach to achieve modeling and parameter estimation simultaneously while Chapter 3 takes a frequentist approach. The main aspects of these techniques are that they perform variable selection and parameter estimation simultaneously, while also being easily adaptable to large-scale data. In particular, by embedding a logistic model into traditional spike and slab framework and selecting of proper prior …

Dynamic System Discovery With Recursive Physics-Informed Neural Networks, Jarrod Mau

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

This thesis presents a novel method, recursive Physics informed neural network, to learn the right hand side of differential equations. The neural network takes in data, then trains, and then acts as a proxy for the differential equation which can be used for modeling. We show the theoretical superiority of the recursive approach. We also use computer simulations to demonstrate the proved properties.

Redefining Nba Basketball Positions Through Visualization And Mega-Cluster Analysis, Alexander L. Hedquist

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

Basketball players have historically been classified based on one of five positions, namely Point Guards, Shooting Guards, Small Forwards, and Centers. While grouping players into these five categories may provide general descriptions of their perceived role, these standard positions fall short of describing players based on their true abilities and performance. This MS thesis proposes a method to group players of the National Basketball Association (NBA) from the past 20 seasons into more meaningful and specific player positions. We systematically group these players into nine distinct categories, and we draw from a vast array of visualization tools, techniques, and software …

An Introduction To Combinatorics Via Cayley's Theorem, Jaylee Willis

All Graduate Theses and Dissertations, Spring 1920 to Summer 2023

In this paper, we explore some of the methods that are often used to solve combinatorial problems by proving Cayley’s theorem on trees in multiple ways. The intended audience of this paper is undergraduate and graduate mathematics students with little to no experience in combinatorics. This paper could also be used as a supplementary text for an undergraduate combinatorics course.

Robust Uncertainty Quantification With Analysis Of Error In Standard And Non-Standard Quantities Of Interest, Zachary Stevens

Mathematics & Statistics ETDs

This thesis derives two Uncertainty Quantification (UQ) methods for differential equations that depend on random parameters: (\textbf{i}) error bounds for a computed cumulative distribution function (\textbf{ii}) a multi-level Monte Carlo (MLMC) algorithm with adaptively refined meshes and accurately computed stopping-criteria. Both UQ approaches utilize adjoint-based \textit{a posteriori} error analysis in order to accurately estimate the error in samples of numerically approximated quantities of interest. The adaptive MLMC algorithm developed in this thesis relies on the adjoint-based error analysis to adaptively create meshes and accurately monitor a stopping criteria. This is in contrast to classical MLMC algorithms which employ either a …

Machine Learning Model Comparison And Arma Simulation Of Exhaled Breath Signals Classifying Covid-19 Patients, Aaron Christopher Segura

Mathematics & Statistics ETDs

This study compared the performance of machine learning models in classifying COVID-19 patients using exhaled breath signals and simulated datasets. Ground truth classification was determined by the gold standard Polymerase Chain Reaction (PCR) test results. A residual bootstrapped method generated the simulated datasets by fitting signal data to Autoregressive Moving Average (ARMA) models. Classification models included neural networks, k-nearest neighbors, naïve Bayes, random forest, and support vector machines. A Recursive Feature Elimination (RFE) study was performed to determine if reducing signal features would improve the classification models performance using Gini Importance scoring for the two classes. The top 25% of …

Stability And Differential Privacy Of Stochastic Gradient Methods, Zhenhuan Yang

Legacy Theses & Dissertations (2009 - 2024)

Recently there are a considerable amount of work devoted to the study of the algorithmic stability as well as differential privacy (DP) for stochastic gradient methods (SGM). However, most of the existing work focus on the empirical risk minimization (ERM) and the population risk minimization problems. In this paper, we study two types of optimization problems that enjoy wide applications in modern machine learning, namely the minimax problem and the pairwise learning problem.

Multiple Imputation In High-Dimensional Data With Variable Selection, Qiushuang Li

Legacy Theses & Dissertations (2009 - 2024)

This dissertation focuses on the development of multiple imputation models and algorithms for high-dimensional data with variable selection structures. Leveraging on the multivariate linear mixed-effects model with missing responses for clustered data, we incorporate the variable selection routines using spike-and-slab priors within the Bayesian variable selection framework. Specific choice of these priors allow us to "force'' variables of importance (e.g. design variables or variables known to play role in missingness mechanism) into the imputation models. Our ultimate goal is to improve computational speed by removing unnecessary variables. Markov chain Monte Carlo techniques have been designed to sample from the implied …

Development Of A Reverse Engineered, Parameterized, And Structurally Validated Computational Model To Identify Design Parameters That Influence American Football Faceguard Performance, William Ferriell

All Dissertations

Traumatic brain injury (TBI) continues to have the greatest incidence among athletes participating in American football. The headgear design research community has focused on developing accurate computational and experimental analysis techniques to better assess the ability of headgear technology to attenuate impacts and protect athletes from TBI. Despite efforts to innovate the headgear system, minimal progress has been made to innovate the faceguard. Although the faceguard is not the primary component of the headgear system that contributes to impact attenuation, faceguard performance metrics, such as weight, structural stiffness, and visual field occlusions, have been linked to athlete safety. To improve …

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 …

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 …

Quantum Computing Simulation Of The Hydrogen Molecule System With Rigorous Quantum Circuit Derivations, Yili Zhang

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Quantum computing has been an emerging technology in the past few decades. It utilizes the power of programmable quantum devices to perform computation, which can solve complex problems in a feasible time that is impossible with classical computers. Simulating quantum chemical systems using quantum computers is one of the most active research fields in quantum computing. However, due to the novelty of the technology and concept, most materials in the literature are not accessible for newbies in the field and sometimes can cause ambiguity for practitioners due to missing details.

This report provides a rigorous derivation of simulating quantum chemistry …

A Bayesian Hierarchical Approach For Modeling Virtual Species With Realistic Functional Trait Relationships, Sarah Bogen

All Graduate Plan B and other Reports, Spring 1920 to Spring 2023

Understanding the spatial and temporal dynamics of plant populations has important implications for the fields of ecology and conservation. A rich body of mathematical modeling approaches, including reaction-diffusion equations and integrodifference equations, have been developed to mechanistically model population spread based on species demography and seed dispersal characteristics. However, with over 390,000 plant species on Earth, it is not feasible to collect complete information on all species for the purpose of drawing generalized conclusions. One means of overcoming such a problem is through trait-based modeling, which seeks to represent realistic combinations of organismal traits rather than focusing on individual species. …

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 …

Human Perception Of Exponentially Increasing Data Displayed On A Log Scale Evaluated Through Experimental Graphics Tasks, Emily Robinson

Department of Statistics: Dissertations, Theses, and Student Research

Log scales are often used to display data over several orders of magnitude within one graph. We conducted a series of three graphical studies to evaluate the impact displaying data on the log scale has on human perception of exponentially increasing trends compared to displaying data on the linear scale. Each study was related to a different graphical task, each requiring a different level of interaction and cognitive use of the data being presented. The first experiment evaluated whether our ability to perceptually notice differences in exponentially increasing trends is impacted by the choice of scale. Participants were shown a …

A Computationally Efficient Wald Test In M-Estimation, Denisse Urenda Castañeda

Open Access Theses & Dissertations

Under the maximum likelihood framework, three asymptotic overall tests have been well developed in generalized linear models (GLM) for testing the single null hypothesis H0 : θ = θ0, namely, the Wald test, Likelihood Ratio Test (LRT) and Score test also known as the Lagrange Multiplier test (LM). Modified versions of Wald, LR and LM tests can also be found for testing the significance of a portion of the parameter θ, i.e., if θ = (θ T 1 , θ T 2 ) T it is of interest to test H0 : θ2 = 0. However, with the constant increase …

Efficient Approaches To Steady State Detection In Multivariate Systems, Honglun Xu

Open Access Theses & Dissertations

Steady state detection is critically important in many engineering fields such as fault detection and diagnosis, process monitoring and control. However, most of the existing methods are designed for univariate signals. In this dissertation, we proposed an efficient online steady state detection method for multivariate systems through a sequential Bayesian partitioning approach. The signal is modeled by a Bayesian piecewise constant mean and covariance model, and a recursive updating method is developed to calculate the posterior distributions analytically. The duration of the current segment is utilized to test the steady state. Insightful guidance is provided for hyperparameter selection. The effectiveness …