Statistics and Probability Commons^™

A Temporal Extension Of Tasselnet With Uncertainty Quantification Using Bayesian Latent Autoregressive Model, Gayara Demini Fernando Muthunama Gonnage

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

Manual counting of maize tassels remains a time-consuming and labor-intensive process. Automating tassel counting using maize field images has received significant interest, with researchers exploring methods to streamline this task. Accurate tassel detection can substantially reduce both time and labor costs, offering a practical solution for large-scale agricultural management. Current studies in this area predominantly focus on training object detection algorithms to enhance prediction accuracy and efficiency. Images for this task are often captured in sequences, meaning they capture the exact location of the maize fields over time. However, existing tassel detection and counting methods typically ignore this sequential nature, …

Curvilinear Image Segmentation Using Multiscale Variational U-Net, Rebekah Fortes

LSU New Orleans Theses and Dissertations

Segmentation of curvilinear structures such as water contours, cracks in cement, and vascular networks in biomedical imaging, poses unique challenges due to extreme class imbalance, irregular morphology, low contrast against complex backgrounds, and the need to preserve global connectivity while detecting fine-scale details. We propose a Multiscale Variational U-Net (MSVU-Net) architecture designed specifically to address these challenges. The model integrates multiscale convolutional filters to capture both global context and local detail, while embedding a variational model in the bottleneck layer to enhance structural representation. To mitigate class imbalance and improve fidelity, the network optimizes a hybrid loss function that combines …

Coupled Machine Learning Models: Combining Observations And Numerical Analysis In A Physics-Regularized Approach, Austin B. Schmidt

LSU New Orleans Theses and Dissertations

This dissertation investigates surrogate modeling for fixed-location environmental forecasting using novel data-combination techniques. The work surveys the landscape of observational measurements and numerically generated data, identifying similar research and gaps in current methodologies. The ratio-coupled training framework is introduced to combine two data sources per predicted feature through a tunable parameter that weights training signal strength. An optimization scheme is developed to simultaneously tune surrogate weights and the coupled signal ratio, allowing relative influence between signals to act as an explicit regularizer. Three case studies demonstrate the methodology and approach in a variety of contexts. The first study is based …

Flexible Spatial Priors In Bayesian Neuroimaging: Gmrf, Nngp, And Deep Gmrf, Boyoung Hur

All Dissertations

Structural neuroimaging is essential for understanding neurological disorders such as Alzheimer’s disease, enabling accurate delineation of brain regions through image segmentation. Among various segmentation methods, multi-atlas-based approaches like label fusion have become leading techniques. In statistics, Bayesian hierarchical models for label fusion are increasingly favored for their ability to incorporate uncertainty and prior knowledge. Also, a key challenge in modeling neuroimaging data is spatial dependence among image voxels, making the choice of spatial prior critical—particularly in high-resolution settings where segmentation accuracy and computational efficiency are both essential.

This dissertation proposes fully Bayesian spatial hierarchical models that explore two flex- ible …

The Moderating Effect Of Income Inequality On The Income–Emissions Relationship In G20 Countries, Zahra Rizky Fadilah, Budiasih Budiasih

Economics and Finance in Indonesia

This study analyzes the moderating effect of income inequality on the income–emissions relationship in the environmental Kuznets curve (EKC) framework. Findings indicate that the relationship is inverted U-shaped in middle-income G20 countries, but monotonically increasing in high-income G20 countries. Interestingly, income inequality moderates this relationship only in the latter group. These findings suggest that middle-income G20 countries should focus on raising income per capita to mitigate environmental degradation, while their high-income counterparts need to prioritize reducing income inequality to effectively decouple income from emissions.

(R2115) Analysis Of Single Server Queueing System With Differentiated Vacations And Differentiated Breakdowns, V. Karthick, V. Suvitha

Applications and Applied Mathematics: An International Journal (AAM)

This research work considers a single server queueing model with differentiated vacations. In addition there is a possibility of two types of failures when the server is in a busy period; namely hard failure and soft failure. In the time of soft failure server may work with a slow service rate. We analyzed as a Quasi-Birth-and-Death (QBD) process, using the matrix geometric method, the steady state probability vector of the number of customers in the queue and the stability conditions are produced. Busy period analysis of the proposed model in given. The effects of various parameters on the system performance …

(R2132) A Multi Server Markovian Working Vacation Queue With Randomly Varying Environment, A. Sundaramoorthy, R. Kalyanaraman

Applications and Applied Mathematics: An International Journal (AAM)

In this article, we consider a multi server Markovian queueing system with working vacation. During busy period, the arrival and service completion are generated by K distinct randomly varying environments. At a service completion epoch, if no customer in the system, the servers take vacation, the vacation policy is multiple vacation policy and the vacation period follows negative exponential distribution. In addition, during vacation period the servers serve customers if they arrive. Based on the vacation termination point we define two Models. For the two models, the steady state probability vector of number of customers in the queue, the stability …

Content And Consequences: Impact Of Representation In Stem Higher Education Instructional Content On Marginalized Students, Nichole Ventura

Doctorate in Education

This qualitative study examined representation of historically marginalized students in STEM instructional content at the higher education level and its impact on their learning experiences. Despite growing diversity initiatives in STEM enrollment, curricular materials often fail to reflect the identities of underrepresented students. Using critical theory and interpretivist approaches, this research investigated how representation—or its absence—shapes students' sense of belonging, academic identity formation, and persistence. Through semi-structured interviews with undergraduate students from historically marginalized backgrounds, and purposeful sampling, this study captured the lived experiences of students engaging with STEM instructional materials. Interview protocols explored how students perceive their representation in …

Changepoint Detection As Model Selection: A General Framework, Michael A. Grantham

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

This dissertation presents a general framework for changepoint detection based on ℓ₀ model selection. The core method, Iteratively Reweighted Fused Lasso (IRFL), improves upon the generalized lasso by adaptively reweighting penalties to enhance support recovery and minimize criteria such as the Bayesian Information Criterion (BIC). The approach allows for flexible modeling of seasonal patterns, linear and quadratic trends, and autoregressive dependence in the presence of changepoints.

Simulation studies demonstrate that IRFL achieves accurate changepoint detection across a wide range of challenging scenarios, including those involving nuisance factors such as trends, seasonal patterns, and serially correlated errors. The framework is …

Two Topics In Survival Analysis: Restricted Distance Covariance Test For Non-Proportional Hazard And A New Estimation For Dropout Rate, Ruizhe Yin

Graduate Theses and Dissertations

When treatment effects change over time, standard statistical methods, such as the log-rank test and the Cox proportional hazards model, may give misleading results. This dissertation presents the restricted distance covariance (rdcov) test, a nonparametric method that compares survival curves between groups within a chosen study period [0, τ] using right censored data. The statistic measures the dependence between pre-specified group labels and survival times using pairwise distances from Kaplan-Meier estimates. Our method does not rely on the proportional hazards assumption, and it equals zero only when survival functions are identical across groups. Thus, this test can be applied to …

Stochastic Functional Data-Driven Models For Real-Time Battery Health Forecasting Under Dynamic Operating Conditions, Joshua Owusu

Electronic Theses and Dissertations

This thesis provides an effective statistical model to predict the real-time state of lithium-ion batteries for reliable Battery Management Systems (BMS). It highlights battery data (voltage, current, temperature) as smooth functional curves. The principal method demonstrates diminishing trends to health outcomes like State of Health (SoH) and Remaining Useful Life (RUL) by employing Functional Principal Component Analysis (FPCA) and Bayesian Functional Linear Models (FLMs). The primary objective is to figure out how uncertain forecasts are. Simulations demonstrate that the highest accuracy (lowest MSE) is achieved through low noise levels along with large sample sizes. The final system provides a highly …

On Bayesian Empirical Likelihood-Based Method For Complex Survey Data With Application To Non-Probability Sampling, Md Hasibur Rahman

Department of Statistics: Dissertations, Theses, and Student Research

This thesis develops a Bayesian empirical likelihood (BEL) framework for inference under complex survey designs and extends it to non-probability sampling. Parametric likelihood based methods are difficult to apply to complex survey data because the likelihood is rarely available in closed form. EL provides a flexible alternative by replacing the parametric likelihood with an empirical likelihood constructed from moment conditions. The proposed method first integrates empirical likelihood constraints with survey design features then extends BEL to non-probability sampling through selection models and design consistent restrictions. Posterior inference is carried out using a Metropolis–Hastings MCMC algorithm. A real-data analysis further illustrates …

(R2130) Cusum-Test For Unconditional Variance Change Detection In Bilinear Garch Models, Edoh Katchekpele, Abdou Kâ Diongue, Ben Célestin Kouassi

Applications and Applied Mathematics: An International Journal (AAM)

We examine CUSUM-type test for detecting changes in unconditional variance within Bilinear GARCH models. We derive the asymptotic distribution of the test statistic under both null and alternative hypotheses and assess test effectiveness in identifying single structural breaks. Simulation studies support our theoretical results and demonstrate the practical utility of the test.

Copula-Based Tests For Assessing The Association Between Genetic Variants And Mixed Phenotypes, Martin Amoah

Open Access Theses & Dissertations

High-dimensional omics studies increasingly involve heterogeneous data types and phenotypes, which traditional association methods struggle to model jointly due to incompatible marginal distributions and complex dependence structures. This thesis develops a unified copula-based framework for assessing associations between genetic variants and mixed phenotypes by decoupling flexible marginal models from their joint dependence structure. While previous copula-based approaches in this setting have focused largely on continuous and binary traits, we extend these methods to a broader class of phenotype pairs. Specifically, we introduce new association tests for bivariate outcomes involving ordinal–continuous, nominal–continuous, and survival–continuous combinations. The proposed methodology derives joint density …

Instance-Adaptive Gated Fusion Of Multi-Transform Image Representations, Prince Appiah

Open Access Theses & Dissertations

This dissertation proposes the Instance-Adaptive Gated Fusion (IAGF) framework, a novel deep learning architecture for adaptive and interpretable fusion of multiple time–series image transformations. While existing methods rely on static concatenation or dataset-level optimization, IAGF introduces a learnable gating mechanism that dynamically assigns per-instance weights to Recurrence Plots (RP), Gramian Angular Summation Fields (GASF), and Gramian Angular Difference Fields (GADF). The gating layer performs a convex fusion of transformation-specific embeddings under a softmax constraint, ensuring mathematical stability and interpretability. An entropy-regularized objective prevents dominance collapse and promotes balanced exploration of transformations during training. Comprehensive experiments across eighteen benchmark datasets, spanning …

Multi-Hop Hybrid Graph Neural Network, James Arthur

Open Access Theses & Dissertations

Graph-structured data appear across diverse domains, such as social networks, citation graphs, biological systems, and knowledge bases. Graph Neural Networks (GNNs) have emerged as a powerful framework for learning on such data, yet existing architectures face significant challenges. Graph Convolutional Networks (GCNs) suffer from over-smoothing as depth increases, Graph Attention Networks (GATs) introduce computational and statistical instabilities, and naïve multi-hop propagation inflates memory and computation while failing to adapt to topology. These limitations motivate the development of a new framework that is both expressive and scalable. This dissertation proposes the Multi-Hop Hybrid Graph Neural Network (MHHGNN), a novel architecture that …

A Unified Framework For Embedding-Based Synthetic Data Generation With High Cardinality Categorical Features, Cesar Iram Vazquez

Open Access Theses & Dissertations

High-cardinality categorical variables remain difficult to model in tabular data, where classical encoders encounter sparsity, susceptibility to leakage, and the loss of meaningful relational structure. This dissertation develops a unified framework for learning, evaluating, and synthesizing representations of such variables using both traditional encoders and modern embedding methods, including Word2Vec, FastText, Node2Vec, TF–IDF/SVD, and supervised entity embeddings. The framework is applied across three benchmark datasets (Adult, PetFinder, Breast Cancer) and a hierarchical educational case study (IPEDS/CIP). Embedding quality is examined through both downstream predictive performance and structure-focused diagnostics that quantify neighborhood behavior and geometric coherence. To assess whether synthetic data …

The Presentation Of Self In Everyday Digital Life: A Study Of Self- Disclosure And Work Environments, Mackenzie Michelle Skiff

Communication & Theatre Arts Theses

The digital age impacts individuals’ lives in many ways. One impact is how and where work is completed across many careers. The work-from-home strategy enables individuals to complete work that is not within a shared space, such as an office. With the absence of this shared space, communication practices within workplaces could be changing. Specifically, self-disclosure while working from home may differ from self-disclosure within the office or hybrid (both in office and remote) work environments. This thesis investigates whether there are differences in self-disclosure practices across three different types of contemporary work environments and offers a digital update to …

Postprocessing Gan-Generated Synthetic Time Series Using Dynamic Time Warping, Md Raisul Islam Roni

Electronic Theses and Dissertations

Generative Adversarial Networks (GANs) are a class of deep learning models capable of producing realistic synthetic data that preserve the statistical and temporal characteristics of real datasets. The DoppelGANger (DGAN) framework extends this approach to time series data by jointly modeling temporal dependencies and contextual metadata. However, synthetic sequences generated by GAN may show temporal misalignment, resulting in inconsistencies when compared with real data. This study presents a postprocessing framework based on Dynamic Time Warping (DTW) and its differentiable extension Soft-DTW to improve the temporal alignment of synthetic time series. The framework is evaluated using quantitative measures of alignment and …

Tradespace Exploration For Multiple Collaborative Vehicles, Mrunal Deshmukh

All Theses

Modern military operations demand systems that adapt to uncertain, rapidly changing missions across diverse terrains. Traditional single-platform vehicle design is insufficient for such complexity. This research introduces a hierarchical tradespace exploration framework for designing and evaluating families of heterogeneous ground vehicles under a System-of-Systems (SoS) architecture. The framework treats vehicle design as a co-optimization problem, where a “parent” vehicle (e.g., a Squad Multipurpose Equipment Transport) coordinates specialized “child” vehicles for reconnaissance, amphibious tasks, terrain traversal, and stealth missions. Unlike conventional approaches that optimize vehicles individually, this study emphasizes collaborative performance, resource sharing, and adaptability at the family level. Central to …

A Leslie System For A Demographic Simulation: From An Actuarial Point Of View, David Kings

Electronic Theses and Dissertations

This thesis develops a discrete stochastic linear systems interpretation of age–stage demographic evolution grounded in Leslie operators and realized in a discrete-event simulation implemented with salabim. The central claim is that one annual cycle of the simulation constitutes a cone-preserving, stochastic affine transformation on a high- dimensional population state vector indexed by age, sex, marital status, household type, employment, and education, and that the composition of yearly operators yields a random matrix product whose top Lyapunov exponent is the stochastic counterpart of the Perron–Frobenius growth rate (Caswell, 2001; Tuljapurkar, 1997)[1, 2]. The actuarial bridge is constructed by mapping simulated survival …

A Persistent Homology Framework For Scrna-Seq: Assessing Clustering Robustness And Quantifying Preprocessing And Integration Effects On Topological Features., Jonah Daneshmand

Electronic Theses and Dissertations

As single-cell RNA sequencing (scRNA-seq) data expands, robust methods for integrating diverse datasets are critical. This dissertation applies Persistent Homology (PH), a technique from Topological Data Analysis (TDA), to a collection of scRNA-seq datasets spanning eight tissue types to quantify how data integration affects topological features and biological interpretability. We assessed global topological structure using Betti curves, Euler characteristics, and persistence landscapes across raw, normalized, and integrated data representations. Our analysis revealed a performance inversion: while conventional methods excelled on unintegrated data, high-granularity topological methods, particularly those sensitive to global data structure, became superior after integration. This suggests a synergy …

Contributions To Statistical Modeling And Estimation Of Rainfall Intensity–Duration–Frequency Curves, Jiyun Huang

All Dissertations

Extreme rainfall can cause flooding, damage infrastructure, and create serious risks for communities. Engineers use Intensity-Duration-Frequency (IDF) curves to estimate precipitation extremes over different lengths of time, such as one hour or one day, and the average time one would expect wait for one of these events to occur. Several approaches exist for estimating IDF curves, but no single approach is known to be best in all cases. In this dissertation, I study statistical methods that aim to improve IDF curve estimation. I use the Canadian Regional Climate Model (CanRCM4) Large Ensemble, which contains 35 independent climate simulations. These simulations …

Error Reduction Methodology And Data Simulation For Interval Data, Ranik Christopher Jelinek

Undergraduate Honors Capstone Projects

Chronic kidney disease (CKD) is a progressive condition affecting hundreds of millions of individuals worldwide. However, clinical datasets often record continuous laboratory measurements as categorical intervals rather than precise numerical values. This interval-censored structure presents methodological challenges for standard regression-based classifiers. This study compares three strategies for handling interval-valued predictors prior to fitting a logistic LASSO model: (1) midpoint imputation, which replaces each interval with its arithmetic center; (2) ordinal encoding, which maps intervals to integer ranks; and (3) a Monte Carlo simulation approach, which repeatedly samples uniformly from each observed interval and averages predictions across replications. Using a 10-fold …

Taxonomy Of Endophytic Fungi Associated With Vallisneria Neotropicalis, Md. Arafat Rashid

Graduate Theses and Dissertations (2019 - present)

This study presents the first comprehensive taxonomic and ecological investigation of endophytic fungi (EF) associated with the submerged aquatic macrophyte Vallisneria neotropicalis in the southern United States. Over a 12 month period, from April 2023 to March 2024, leaf samples were collected from two distinct sites in Mobile Bay, Alabama a disturbed, brackish Causeway location and a cleaner, less impacted site at Meaher State Park. Using culture dependent methods, a total of 257 fungal endophytes were isolated from 1,200 leaf segments. All isolates belonged to the phylum Ascomycota, distributed across 3 classes, 6 orders, 10 families, and 19 taxa. The …

Human Capital, Immigration, And Growth: A State-Level Dynamic Panel Study, William R. Cooper

Graduate Theses and Dissertations (2019 - present)

This study examines whether who immigrates, rather than how many, matters for state economic growth in the United States. It integrates a policy-relevant proxy for skill (H-1B approvals) into an augmented Solow framework that separates immigration's quantity channel from its human capital channel and estimates dynamic effects in a balanced quarterly panel of 50 states (2010 to 2023 ). The empirical strategy estimates a two-step difference GMM Arellano-Bond model that reinforces identification using a double/debiased machine learning (DML) variant that orthogonalizes high-dimensional nuisance components via cross-fitting. This design targets the distinct roles of immigrant headcount versus skill in per capita …

Short-Term Preeclampsia Prediction: Cutoff Variations For Sflt-1/Plgf In U.S. Patients With Or Without Hypertensive Disorders, Yaxin Li, Kristen Cagino, Jim Yee, Caroline Andy, Dajana Borova, Ayush Shah, Isla Racine, Tracy Grossman, Zhen Zhao

Student Papers, Posters & Projects

BACKGROUND: Preeclampsia (PE) is a complex disorder with significant maternal and fetal risks. The soluble fms-like tyrosine kinase-1 (sFlt-1) and placental growth factor (PlGF) ratio shows promise as a diagnostic tool, but its adoption in the U.S. remains limited due to the lack of accessible testing platforms, U.S.-based studies, and evidence-based implementation guidelines.

PATIENTS/MATERIALS AND METHODS: We conducted a cohort study to evaluate the sFlt-1/PlGF ratio for predicting PE within two weeks among pregnant individuals ≥18 years, ≥20 weeks gestation. Serum samples were obtained from routine prenatal visits or triage evaluations. sFlt-1/PlGF ratios were measured using Roche Elecsys assays, and …

Component Model Development Of Heat Exchangers, Expanders, And Control Valves For Autonomous Cryogenics Plant Cool-Down, William Harris Buhrig Iv

Mechanical & Aerospace Engineering Theses & Dissertations

The traditional method of cryogenic plant cool-down involves having continuous on-call staff to head into the office at any time to modify the existing multi-layered PID control systems if the on-call staff member detects a significant deviation from the cool-down plan. This thesis aims to outline an effective method for modeling the structure of systems with performance characteristics that deviate from design requirements and from ideal inlet-outlet correspondence, enabling the adjustment and modification of existing control structures across all Thomas Jefferson National Accelerator Facility (JLab) cryogenic refrigeration plants. Analytical Modeling and Gaussian Process Regression (GPR) are applied to model the …

The Impact Of Transmission Thresholds Across Multiple Scales On The Spread Of Chronic Wasting Disease In Wisconsin, Jen Mcclure

All Graduate Theses and Dissertations, Fall 2023 to Present

Wildlife diseases can be difficult to control once they are established. This is especially true when they spread through contact with infectious material left in the environment. One such disease is chronic wasting disease (CWD), a fatal illness affecting deer and related species in North America and other regions. CWD is caused by prions, misfolded proteins that can remain infectious for years after shedding by infected hosts.

Recent research shows that CWD infection does not always follow from the gradual accumulation of prions through small contact events. Rather, an individual may need to encounter a certain prion dose all at …

Implications Of The Attenuated Allee Effect On Population Dynamics, Dana Strong

All Graduate Theses and Dissertations, Fall 2023 to Present

The Allee effect is an ecological phenomenon characterized by a per capita growth rate that increases as the population size increases in the context of low population density. The Allee effect can lead to rapid extinction events. Consequently, ecologists can attempt to control the strength of the Allee effect to help increase the population (in the case of endangered species) or decrease the population (in the case of parasites or pests).

This research aimed to study not the strength of the Allee effect, but the intensity of the Allee effect, or how rapidly cooperation between individuals causes the per capita …