Statistical Models Commons^™

Utility In Time Description In Priority Best-Worst Discrete Choice Models: An Empirical Evaluation Using Flynn's Data, Sasanka Adikari, Norou Diawara

Mathematics & Statistics Faculty Publications

Discrete choice models (DCMs) are applied in many fields and in the statistical modelling of consumer behavior. This paper focuses on a form of choice experiment, best-worst scaling in discrete choice experiments (DCEs), and the transition probability of a choice of a consumer over time. The analysis was conducted by using simulated data (choice pairs) based on data from Flynn's (2007) 'Quality of Life Experiment'. Most of the traditional approaches assume the choice alternatives are mutually exclusive over time, which is a questionable assumption. We introduced a new copula-based model (CO-CUB) for the transition probability, which can handle the dependent …

Redesign, Evaluation, And Validation Of A Commercially Viable High-Resolution Melt Based Mixture Screening Tool, Chastyn Smith

Theses and Dissertations

Analysis of evidentiary samples containing DNA from multiple contributors (“mixtures”) is a time intensive process for a forensic analyst and one where the contributor nature of a sample is not revealed until the end of the traditional forensic workflow. Often, at this stage, retesting or additional testing of mixture samples may not be possible, particularly if the DNA collection device did not preserve the DNA well enough; consequently leaving only trace amounts of a contributor’s DNA present. Thus, a new collection device that would allow for the increased preservation/integrity of evidentiary samples as well as a method that would allow …

Sparse Representation Learning For Temporal Networks, Maxwell Mcneil

Electronic Theses & Dissertations (2024 - present)

Temporal networks arise in many domains including activity of social network users, sensor network readings over time, and time course gene expression within the interaction network of a model organism. Data of this type contains a wealth of prior information such as the connectivity among nodes (e.g., a friendship graph), and prior knowledge of expected temporal patterns (e.g., periodicity). Modeling these temporal and network patterns jointly is essential for state-of-the-art performance in temporal network data analysis and mining. Sparse dictionary encoding is one modeling approach for such underlying patterns. However, most classical approaches consider only one dimension of the data …

Investigation Of Space Charge Effects On Co2 Electrocatalytic Reduction On Gd-Doped Ceria Via Scanning Kelvin Probe And Model-Based Bayesian Analysis, Alejandro Mejia

Graduate Theses, Dissertations, and Problem Reports (ETD)

In studying novel energy conversion and storage systems, such as high-temperature electrolysis, numerous underlying fundamental physical processes remain unclear or inadequately understood. Among these, the modeling and comprehension of surface reaction mechanisms, coupled with the intricate effects of space‑charge interfaces, remains an unclear and challenging area of research.

The work of this dissertation involves the development of a 2D finite element analysis model, leveraging the robust MOOSE framework from INL. This model, featuring inhomogeneous defect thermodynamics for near-surface chemistry, formulated through Poisson‑Cahn variational theory, has been exploited for studying the electrocatalytic reduction of CO₂ on gadolinia doped ceria. The …

Data-Driven Modeling Of Oxygen Kinetics In La0.6sr0.4co0.2fe0.8o3−Δ (Lscf) For High-Temperature Reduction Of Co2 In An Electrolysis Cell, Ferron Campbell

Graduate Theses, Dissertations, and Problem Reports (ETD)

Electrolysis systems are critical to several societal applications, particularly energy storage and conversion. Developing these systems requires a detailed knowledge of the chemistry and thermodynamics of the materials used in the electrolysis cell. This work focuses on using embedded scientific machine learning as an efficient way to build an interpretable model for the reaction and transport kinetics in the LSCF electrode, whose performance directly influences the electrolysis system’s performance. The models developed in this study are trained using the publicly available machine learning package, FoKL-GP. This package incorporates a robust Gibbs sampler that employs a forward variable selection process to …

Reducing Food Scarcity: The Benefits Of Urban Farming, S.A. Claudell, Emilio Mejia

Journal of Nonprofit Innovation

Urban farming can enhance the lives of communities and help reduce food scarcity. This paper presents a conceptual prototype of an efficient urban farming community that can be scaled for a single apartment building or an entire community across all global geoeconomics regions, including densely populated cities and rural, developing towns and communities. When deployed in coordination with smart crop choices, local farm support, and efficient transportation then the result isn’t just sustainability, but also increasing fresh produce accessibility, optimizing nutritional value, eliminating the use of ‘forever chemicals’, reducing transportation costs, and fostering global environmental benefits.

Imagine Doris, who is …

Microplate-Like Metal Pyrophosphate Engineered On Ni-Foam Towards Multifunctional Electrode Material For Energy Conversion And Storage, Rishabh Srivastava

Electronic Theses & Dissertations

High clean energy demand, dire need for sustainable development, and low carbon footprints are the few intuitive challenges, leading researchers to aim for research and development for high-performance energy devices. The development of materials used in energy devices is currently focused on enhancing the performance, electronic properties, and durability of devices. Tunning the attributes of transition metals using pyrophosphate (P₂O₇) ligand moieties can be a promising approach to meet the requirements of energy devices such as water electrolyzers and supercapacitors, although such a material’s configuration is rarely exposed for this purpose of study.

Herein, we grow …

Differentiation Of Human, Dog, And Cat Hair Fibers Using Dart Tofms And Machine Learning, Laura Ahumada, Erin R. Mcclure-Price, Chad Kwong, Edgard O. Espinoza, John Santerre

SMU Data Science Review

Hair is found in over 90% of crime scenes and has long been analyzed as trace evidence. However, recent reviews of traditional hair fiber analysis techniques, primarily morphological examination, have cast doubt on its reliability. To address these concerns, this study employed machine learning algorithms, specifically Linear Discriminant Analysis (LDA) and Random Forest, on Direct Analysis in Real Time time-of-flight mass spectra collected from human, cat, and dog hair samples. The objective was to develop a chemistry- and statistics-based classification method for unbiased taxonomic identification of hair. The results of the study showed that LDA and Random Forest were highly …

Developing A Precision Agriculture Framework To Assess Financial Viability Of Decisions In Farming And Conservation, Jennifer Sublett

Theses and Dissertations

Agricultural producers are invested in managing the impacts of crop damage on their yields and profit. When damage occurs early enough in an agricultural growing season, farmers have the option to replant their corn stand in an effort to recoup some of the lost profits. In this thesis two different types of naturally occurring damage, wildlife depredation and persistent weed or insect patches, were simulated on two representative regions of Mississippi. These data were then used to assess the financial viability of a range of damage mitigation methods, including partial replanting, enrollment into a government conservation buffer, and no action. …

Analyzing The Efficacy Of Covid-19 Travel Bans: A Regression Analysis Approach, Mallory Kochanek

Honors Projects

Some might associate the term ‘public health’ with the pandemic that occurred in 2020. COVID-19 spread like most have never seen in their lifetime. It is useful to look at the effectiveness of the travel re- strictions in mitigating the spread of the global pandemic. Using linear regression and network regression, we obtain parameter estimates to determine the relation of predictors, such as network effect, percentage of urban population and GDP, on the COVID-19 incidence rate for the months January to April of 2020. Linear regression does not ac- count for the correlation structure of the data. Network regression, on …

Generalized Ratio-Product Cum Regression Variance Estimator In Two-Phase Sampling, Isah Muhammad

CBN Journal of Applied Statistics (JAS)

This study develops a flexible and efficient generalized ratio-product cum regression type estimator of population variance utilizing auxiliary variable in two-phase sampling that incorporates the properties of ratio-type and product-type estimators. The properties of the estimator were derived using first order approximation. The theoretical conditions under which the precision and the flexibility of the estimator is better than some classical estimators are also provided. Empirical evidence from five real datasets suggests that the proposed estimator outperforms the classical variance, ratio variance, product, and exponential ratio type estimators in terms of precision and efficiency. The estimator can be utilized to provide …

Modelling The Naira Exchange Rate Dependence Using Static And Time-Varying Copula, Kabir Katata

CBN Journal of Applied Statistics (JAS)

This paper examines the dependence structure of different currencies versus the Nigerian Naira using constant and time-varying copula. Daily Naira/USD, Naira/Yuan, Naira/Pound, and Naira/Euro exchange rates from 23 December 2011 to 12 May 2020 were utilised. We fitted eight constant and time-varying copula families using the exchange rate standardised residuals. The study finds that the Naira exchange rate may be estimated with student t-copula, Symmetrized Joe-Clayton (SJC), or Rotated Gumbel copula models and Autoregressive (AR)– Glosten Jagannathan RunkleGeneralized Autoregressive Conditional Heteroscedastic (GJR-GARCH) (1,1) models with skewed t residuals for margins. The Naira exchange rate returns is timevarying, tail-dependent, and asymmetric. …

Bayesian Strategies For Propensity Score Estimation In Causal Inference., Uthpala I. Wanigasekara

Electronic Theses and Dissertations

Causal inference is a method used in various fields to draw causal conclusions based on data. It involves using assumptions, study designs, and estimation strategies to minimize the impact of confounding variables. Propensity scores are used to estimate outcome effects, through matching methods, stratification, weighting methods, and the Covariate Balancing Propensity Score method. However, they can be sensitive to estimation techniques and can lead to unstable findings. Researchers have proposed integrating weighing with regression adjustment in parametric models to improve causal inference validity. The first project focuses on Bayesian joint and two-stage methods for propensity score analysis. Propensity score modeling …

Radiation Exposure Calibration Of The Al2o3:C With Radium-226 And Cesium-137 Using The Osl Method, Selma Tepeli Aydin

All Theses

Optically stimulated luminescence (OSL) dosimetry was utilized to calibrate Al₂O₃:C powder dosimeters, available commercially as the nanoDot® from Landauer Inc., and compare the dosimeter response to radium-226 (²²⁶Ra) and cesium-137 (¹³⁷Cs). The signal from the OSL was quantified using a microSTARii^® OSL reader also produced by Landauer Inc. Dose-response curves were developed for ²²⁶Ra and ¹³⁷Cs experiments (5 dosimeters each) at thirteen absorbed doses. Individual dosimeter response was tracked by serial number. Linear regression analysis was performed to determine if there were significant differences between the intercepts of the …

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 …

The Private Pilot Check Ride: Applying The Spacing Effect Theory To Predict Time To Proficiency For The Practical Test, Michael Scott Harwin

Theses and Dissertations

This study examined the relationship between a set of targeted factors and the total flight time students needed to become ready to take the private pilot check ride. The study was grounded in Ebbinghaus’s (1885/1913/2013) forgetting curve theory and spacing effect, and Ausubel’s (1963) theory of meaningful learning. The research factors included (a) training time to proficiency, which represented the number of training days needed to become check-ride ready; (b) flight training program (Part 61 vs. Part 141); (c) organization offering the training program (2- or 4-year college/university vs. FBO); (d) scheduling policy (mandated vs. student-driven); and demographical variables, which …

Proteomics And Machine Learning For Pulmonary Embolism Risk With Protein Markers, Yaa Amankwah Awuah

Electronic Theses and Dissertations

This thesis investigates protein markers linked to pulmonary embolism risk using proteomics and statistical methods, employing unsupervised and supervised machine learning techniques. The research analyzes existing datasets, identifies significant features, and observes gender differences through MANOVA. Principal Component Analysis reduces variables from 378 to 59, and Random Forest achieves 70% accuracy. These findings contribute to our understanding of pulmonary embolism and may lead to diagnostic biomarkers. MANOVA reveals significant gender differences, and applying proteomics holds promise for clinical practice and research.

Exploration And Statistical Modeling Of Profit, Caleb Gibson

Undergraduate Honors Theses

For any company involved in sales, maximization of profit is the driving force that guides all decision-making. Many factors can influence how profitable a company can be, including external factors like changes in inflation or consumer demand or internal factors like pricing and product cost. Understanding specific trends in one's own internal data, a company can readily identify problem areas or potential growth opportunities to help increase profitability.

In this discussion, we use an extensive data set to examine how a company might analyze their own data to identify potential changes the company might investigate to drive better performance. Based …

Predicting Dengue Incidence In Central Argentina Using Google Trends Data, Sahil Chindal

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

The Double Edged Sword Of The Pandemic: Exploring Associations Between Covid-19 And Social Isolation In The Usa, Alexander Fulk

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.