Statistics and Probability Commons^™

Sccosmix: A Mixed-Effects Framework For Differential Coexpression And Transcriptional Interactions Modeling In Single-Cell Rna-Seq, Anderson Bussing, Giampiero Marra, Daping Fan, Russell Shinohara, Danni Tu, Yen-Yi Ho

Faculty Publications

Advancements in single-cell RNA-sequencing (scRNA-seq) technologies generate a wealth of gene expression data that provide exciting opportunities for studying gene-gene interactions systematically at individual cell resolution. Genetic interactions within a cell are tightly regulated and often highly dynamic in response to internal cellular signals and external stimuli. Evidence of these dynamic interactions can often be observed in scRNA-seq data by examining conditional co-expression changes. Existing approaches for studying these dynamic interaction changes in scRNA-seq data do not address the multi-subject hierarchical design commonly considered in single-cell experiments. In this paper, we propose a Mixed-effects framework for differential Coexpression and transcriptional …

Estimation Methods For Bayesian Exponential Random Graph Models Under The Horseshoe Prior., Pamela Linares

Electronic Theses and Dissertations

Networks are powerful tools for modeling the complexity of social interactions, biological systems, and information spread. A leading statistical frameworks for analyzing network data are Exponential Random Graph Models (ERGMs), which provide a principled approach to capturing structural dependencies. However, ERGMs remain challenging to estimate, especially in sparse or high-dimensional settings where models suffer from degeneracy and unstable parameter inference. This paper proposes a penalized Bayesian approach to ERGMs that utilizes the horseshoe prior, a sparsity-inducing global-local shrinkage prior. This prior offers robust regularization while preserving important signals, improving estimation by shrinking irrelevant parameters and reducing the impact of extreme …

Predictor-Informed Bayesian Nonparametric Clustering., Md Yasin Ali Parh

Electronic Theses and Dissertations

In this dissertation, we performed clustering of observations such that the cluster membership is influenced by a set of predictors. To that end, we employ the Bayesian nonparametric Common Atom Model (CAM), which is a nested clustering algorithm that utilizes a (fixed) group membership for each observation to encourage more similar clustering of members of the same group. CAM operates by assuming each group has its own vector of cluster probabilities, which are themselves clustered to allow similar clustering for some groups. We extend this approach by treating the group membership as an unknown latent variable determined as a flexible …

Tree-Based Differential Item Functioning Detection Methods: Exploring Their Performance In Diverse Measurement Scenarios, Nana Amma Berko Asamoah

Graduate Theses and Dissertations

Despite the availability of numerous methods for detecting differential item functioning (DIF), the continued development and evaluation of innovative, data-driven approaches remains essential. Tree-based methods, in particular, represent a significant advancement in DIF detection. Unlike some traditional techniques, they can simultaneously screen multiple variables for DIF without discretizing continuous variables, and do not require the pre-specification of focal and reference groups; capabilities that are especially valuable in today’s diverse and multifaceted assessment contexts. However, research systematically examining the performance of these methods under realistic measurement conditions is limited. This dissertation, in three simulation studies, critically examines the robustness and practical …

Multivariate Mixture Regression Models With Known Group Membership And Informative Priors, Pahalapathirage Dona Kalani Hasanthika

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

We introduced couple different novel approaches to incorporate latent variable information to multivariate mixture regression models with both Gaussian and count data. We also evaluated the performance of these models with existing best approaches with simulated data from various sampling structures and also evaluated one of the models performance with rice metabolite data that provided some novel insights as well as validating existing literature about performance and behavior of these metabolites. We validated the method using extensive simulations and a real-world application. In both quantitative covariate designs and complex treatment design simulations, our method consistently outperformed established tools like limma, …

Advancing Real-World Implementation Of The Well Optimized Linear Finder (Wolf) High-Speed Atmospheric Turbulence Compensation Method, Timothy Evan Coon

Theses and Dissertations

This dissertation advances the real-world implementation of the Well Optimized Linear Finder (WOLF) method for high-speed Atmospheric Turbulence Compensation (ATC). Atmospheric turbulence introduces phase aberrations into optical wavefronts and degrades image quality in terrestrial imaging systems. Traditional phase diversity methods are computationally intensive and poorly suited to real-time operation. The WOLF method addresses these limitations through a novel, point-wise formulation of the optical transfer function (OTF) as a structured autocorrelation of the generalized pupil function (GPF). This formulation enables the estimation of phase aberrations at individual spatial coordinates with distributed computational complexity.

The research begins by developing a MATLAB-based simulation …

Quantifying The Sensitivity Of Land Use Land Cover Metrics Through Simulation Techniques, Haley Burger

All Graduate Theses and Dissertations, Fall 2023 to Present

As human activities and climate change continue to reshape our landscape, understanding how land use changes over time is becoming increasingly important. Accurate ways to track and analyze these changes are essential for governments, businesses, and communities to make informed decisions. Monitoring agricultural land is particularly critical, as shifts in land use can impact food production and environmental pollutants. One of the primary tools used in the United States to monitor agricultural land is the Cropland Data Layer (CDL), an annual map created by the United States Department of Agriculture (USDA) from satellite images. While the CDL is highly accurate, …

Unveiling Insights From Complexity: Advanced Computational Techniques For High-Dimensional Medical Data, Devin P. Eddington

All Graduate Theses and Dissertations, Fall 2023 to Present

Healthcare generates vast amounts of data daily, from genetic profiles to hospital records, but much of it remains untapped due to its complexity. This dissertation develops new computational tools to unlock this data’s potential, aiming to improve patient care and medical research. Five projects tackle different challenges: Project 1 creates Deep MAGIC, a method to fill in missing genetic and image data accurately, vital for understanding diseases like cancer. Project 2 analyzes how the COVID-19 pandemic disrupted surgeries, finding a 27% drop and temporary complication rises in 2020, guiding future crisis planning. Projects 3 and 4 study kidney disease trials, …

Graph-Based Machine Learning: Higher-Order Interactions, Guided Generation, And Knowledge-Graph Tools, Thomas J. Kerby

All Graduate Theses and Dissertations, Fall 2023 to Present

This dissertation brings the power of graph thinking to three key challenges in modern AI, making complex data more transparent, generative design more controllable, and scholarly exploration more intuitive. First, we introduce Local CorEx, a new machine learning technique that uncovers hidden relationships among variables, making it easier to understand complex datasets without heavy computation. Next, we show how to guide the creation of new molecules by viewing the generation process itself as a walk through a "state graph," letting researchers steer outcomes toward desired chemical properties—without any extra model training. Finally, we deliver an open-source toolkit that builds interactive …

Empirical Evaluation Of Bayes Error Rate Bounds In Binary Classification, Riley May

All Graduate Theses and Dissertations, Fall 2023 to Present

Classification tasks are fundamental in statistical machine learning. In classification tasks, a general goal is to build or select a model that can correctly classify data with as few errors as possible. However, for a particular dataset, the minimal number of errors achievable is seldom zero since overlap in the data makes errors unavoidable. As a result, it is often difficult for machine learning practitioners and data scientists to know whether classification errors can be reduced through further refinement. A potential solution to this lies in the Bayes error rate (BER). The BER is the lowest error rate achievable for …

Bayes In The Brain: A Review Of Everything Is Predictable: How Bayesian Statistics Explain Our World, (2024) By Tom Chivers., Michael T. Catalano

Numeracy

Tom Chivers’ Everything is Predictable: How Bayesian Statistics Explain Our World, is an interesting and wide-ranging narrative on Bayesian thinking, its history, and its applicability to both our everyday lives and the pursuit of scientific truth. Although appropriate for the non-expert, afficionados and teachers of quantitative literacy should find the plethora of examples, links to psychology as it applies to how people reason about probabilities, and even Chivers’ philosophical musings informative and thought-provoking.

The Effectiveness Of Remote Patient Monitoring In Reducing The Risk Of Rehospitalizations In Covid-19 Patients: A Meta-Analysis, Dela Riadi, Indang Trihandini, Dewi Nirmala Sari, Fikri Wijaya

Kesmas

An integrated analysis of various Remote Patient Monitoring (RPM) studies is needed to evaluate the reduction rate of the risk of rehospitalization in COVID-19 patients. This meta-analysis aimed to provide an overview of the effectiveness of RPM. A literature search through online databases (PubMed, Science Direct, Scopus, ProQuest, and Embase) was conducted from 2019 to 2022. After using the Cochrane Collaboration's risk of bias tool, five studies on COVID-19 were selected. Based on the data collected from 2,685 participants (intervention = 1,060, control = 1,625), the use of RPM was found to reduce rehospitalization by 0.56 times compared to not …

Cross-Cultural Adaptation And Validation Of Ranas-Based Instrument For Measuring Latrine Use Behavior In Indonesia, Vera Yulyani, Fatwa Sari Tetra Dewi, Iswanto Iswanto

Kesmas

Using toilets is a simple way to prevent diarrhea, yet no validated tool exists to measure this habit. This study aimed to develop and validate instruments for measuring latrine use consistency. This questionnaire was adapted from the risk, attitude, norm, ability, and self-regulation (RANAS) framework developed in India and modified for Indonesia. It was evaluated by three experts using the content validity index (CVI). The face validity index (FVI) was pilot-tested on 40 community respondents. Variables measured included behavior, habits, intentions to use toilets, knowledge, attitudes, norms, abilities, and self-regulation. Question items with relevance and clarity scores of item CVI …

Analyzing High-Risk Fertility Behavior For Sustainable Maternal-Child Health: A 2017 Sociodemographic Study In Urban And Rural Indonesia, Asti Annisa Utami, Fadhaa Aditya Kautsar Murti, Popy Yuniar, Milla Herdayati

Kesmas

Indonesia's goal of achieving Indonesia Emas 2045 hinges on improving Maternal-Child Health (MCH), essential for building a healthy and competitive population. Despite some advancements, the Maternal Mortality Rate (MMR) and Under-five Mortality Rate (U5MR) remain high, particularly because of High-Risk Fertility Behavior (HRFB). The HRFB poses significant risks to MCH, affecting both urban and rural women. This study aimed to identify the factors associated with HRFB in these areas to enhance MCH outcomes and support Indonesia's sustainable health goals. This cross-sectional study used a secondary dataset from the 2017 Indonesian Demographic Health Survey. A total of 20,530 women of reproductive …

Minimal Error Functions On Irregular Subsets Of The Real Line, Robert Michael Dukes

Mathematics & Statistics ETDs

Chebyshev Polynomials, those that minimize the maximal error on a compact set, are one of the most practical tools for approximating smooth functions. The classical results are on the set [-1, 1]; in this paper, we extend to more complicated subsets of the real line. We demonstrate some classical results and then take the result from [2] on regular Parreau-Widom Sets and extend it to semi-regular sets, defined as sets whose regular part is closed. We introduce the Regularity Coefficient as a series formed by evaluating the Green’s Function at irregular points. This new machinery is applied to the lower …

A Data-Driven Approach To Time Series Forecasting And Clustering Of U.S. Regional Drug Overdose Mortality, Koshali Hamy Muthunama Gonnage

Mathematics & Statistics ETDs

The increasing rate of drug overdose deaths in the United States poses a critical public health challenge, particularly due to the surge in synthetic opioids and other high-risk substances. This study presents a data-driven framework that integrates time series forecasting and clustering techniques. Monthly mortality data for five key drug types: cocaine, fentanyl, heroin, methamphetamine, and oxycodone were analyzed using four time series forecasting models: ARIMA, ETS, TBATS, and NNAR. These models were evaluated using standard accuracy metrics RMSE, MAPE, and MAE to assess predictive performance. Signal decomposition approach based on Singular Value Decomposition and subspace modeling was employed to …

The Nature Of Anthropogenically Driven River Drying: Spatiotemporal Causes And Consequences, Eliza Inez Gilbert

Biology ETDs

Streambed drying naturally occurs in over 60% of rivers and streams worldwide. Climate change and human regulation of surface and groundwater have increased drying in naturally intermittent systems and caused perennial systems to transition to intermittency, impacting water security, water quality, and biodiversity. To understand human-induced drying dynamics, we used 12 years of daily drying data along a 154-km regulated reach of the Rio Grande. We conceptualized river drying as a regime analogous to the natural flow regime paradigm and quantified drying magnitude, rate of change, and duration. Although linear models predicting drying magnitude and rate of change were uninterpretable, …

Some Nonparametric Tests For High-Dimensional And Functional Data, Bilol Banerjee

Doctoral Theses

The advancement of information technology and sciences over the last few decades has facilitated the collection, storage and analysis of huge data sets. Many of these data sets contain observations having large number of features, and in some cases, this number is comparable to or even much larger than the sample size. Many traditional statistical methods cannot be meaningfully used in such situations. We develop some inferential tools for such high dimensional data. In particular, we consider the two-sample problem and the problem of testing spherical symmetry of a multivariate distributions. We construct some nonparametric tests in these contexts and …

An Experimental Investigation Of Federal Messaging On Public Support For Enforcement- And Treatment-Based Approaches For Opioid Overdose Prevention In South Carolina, Lídia Gual-Gonzalez, Hunter M. Boehme, Peter Baker, Melissa Nolan Ph.D., Mph

Faculty Publications

Background

As the opioid overdose crisis continues to produce excessive morbidity and mortality in the United States, government agencies have applied various approaches to prevent overdoses, including law-enforcement efforts (e.g., arresting people who use drugs, interrupting drug traffickers, etc.) and treatment-based approaches (e.g., naloxone, medications for opioid use disorder, etc.). Public perception and support of these approaches are relevant for informing policy, allocating resources, and effectively implementing community interventions to prevent drug-related harms.

Methods

Using an embedded informational survey design, we experimentally assessed whether public support for strategies to prevent overdose in South Carolina is influenced by language from federal …

Towards Scalable Taxi Demand Prediction, Yifei Shen

Lingnan Theses (MPhil & PhD)

Accurate taxi demand prediction is essential for optimizing urban mobility systems across varying spatial-temporal resolutions and data conditions. Scalable taxi demand prediction refers to the capability of forecasting models to adapt to different granularities of spatial and temporal data while maintaining prediction accuracy, a critical requirement for practical urban applications ranging from fleet management to transportation planning. However, two fundamental challenges impede this scalability: data sparsity and multi-resolution forecasting requirements. Data sparsity, particularly pronounced in high-resolution predictions where numerous regions exhibit minimal activity, significantly compromises model performance. Concurrently, different urban applications necessitate predictions at varying temporal and spatial granularities, requiring …

The Sodium-Glutamate Antagonist Riluzole Improves Outcome After Acute Spinal Cord Injury: Results From The Riscis Randomised Controlled Trial Analysed Using A Global Statistical Analytic Technique, Michael G. Fehlings, Karlo M. Pedro, Mohammed Ali Alvi, Ali Moghaddamjou, James S. Harrop, Ralph Stanford, Jonathon Ball, Bizhan Aarabi, Paul M. Arnold, James D. Guest, Shekar N. Kurpad, James M. Schuster, Ahmad N. Nassr, Karl M. Schmitt, Jefferson R. Wilson, Darrel S. Brodke, Faiz U. Ahmad, Albert Yee, Wilson Z. Ray, Nathaniel P. Brooks, Jason Wilson, Diana S.L. Chow, Elizabeth G. Toups, Kevin E. Thorpe, Jiaxin Huang, Peng Huang

School of Medicine Faculty Publications

Background: Spinal cord injury (SCI) clinical trials typically rely on a single primary endpoint to assess drug efficacy. This strategy fails to adequately capture the full impact of treatment in heterogenous neurological conditions like SCI. A more patient-centric analysis requires assessment of neurological function, functional capacity, and quality of life, incorporating meaningful patient-reported outcomes. The global statistical test (GST) addresses this challenge using a unified statistical conclusion regarding the superiority of a treatment strategy over another by evaluating multiple trial endpoints simultaneously. Methods: The RISCIS trial (Safety and Efficacy of Riluzole in Acute Spinal Cord Injury Study) data was analysed …

Three-Stage Latent Dynamics Forecasting (T-Ldf) Framework For Shenzhen Metro Passenger Flow Prediction, Tianze Zhang

Lingnan Theses (MPhil & PhD)

Accurate forecasting of metro passenger flow is vital for efficient urban transportation management and optimal resource allocation in modern cities. Traditional ARIMA-based models effectively capture regular, cyclical patterns but struggle with sudden, nonlinear fluctuations caused by random events such as weather disruptions, special events, or service interruptions. Moreover, existing research predominantly focuses on individual stations, overlooking the complex cross-station interactions inherent in networked metro systems where passenger flows are interconnected across the entire network.

To address these critical limitations, we propose the Three-Stage Latent Dynamics Forecasting (T-LDF) Framework, a novel approach that systematically integrates temporal decomposition, latent dynamics extraction, and …

Secondary Malignancies In Patients With Meningioma: A Surveillance, Epidemiology, And End Results Data Analysis, Maxwell W. Pickles, Thomas Z. Rohan, Shreya Vinjamuri, Nikolaos Mouchtouris, Roger Murayi, David P. Bray, James J. Evans

Department of Neurosurgery Faculty Papers

BACKGROUND: The risk of secondary primary malignancies (SPMs) in meningioma patients is not well understood. In this unidirectional analysis, we evaluated the risk of SPMs occurring following a primary diagnosis of meningioma.

METHODS: The Surveillance, Epidemiology, and End Results (SEER-17) database (2000-2020) was used to identify 124,769 meningioma patients from a total of 9,208,295 cancer cases. Standardized incidence ratios (SIRs) were calculated using SEER's statistical analysis package to evaluate SPM risk. Basic demographic and treatment information was collected as well.

RESULTS: Of the 124,769 patients, 11,411 (9.2%) received diagnoses of an SPM, which correlates to a higher risk than the …

Predicting Sleep And Sleep Stage In Children Using Actigraphy And Heartrate Via A Long Short-Term Memory Deep Learning Algorithm: A Performance Evaluation, Robert Weaver Med, Phd, James White, Olivia Finnegan, Hongpeng Yang, Zifei Zhong, Keagan Kiely, Catherine Jones, Yan Tong, Srihari Nelakuditi, Rahul Ghosal, David E. Brown, Russell R. Pate Ph.D., Gregory J. Welk, Massimiliano De Zambotti, Yuan Wang, Sarah Burkart, Elizabeth L. Adams Phd, Bridget Armstrong, Michael Beets Med, Mph, Phd

Faculty Publications

Children's ambulatory sleep is commonly measured via actigraphy. However, traditional actigraphy measured sleep (e.g., Sadeh algorithm) struggles to predict wake (i.e., specificity, values typically < 70) and cannot predict sleep stages. Long short-term memory (LSTM) is a machine learning algorithm that may address these deficiencies. This study evaluated the agreement of LSTM sleep estimates from actigraphy and heartrate (HR) data with polysomnography (PSG). Children (N = 238, 5–12 years,52.8% male, 50% Black 31.9% White) participated in an overnight laboratory polysomnography. Participants were referred be-cause of suspected sleep disruptions. Children wore an ActiGraph GT9X accelerometer and two of three consumer wearables(i.e., Apple Watch Series 7, Fitbit Sense, Garmin Vivoactive 4) on their non-dominant wrist during the polysomnogram. LSTM estimated sleep versus wake and sleep stage (wake, not-REM, REM) using raw actigraphy and HR data for each 30-s epoch. Logistic regression and random forest were also estimated as a benchmark for performance with which to compare the LSTM results. A 10-fold cross-validation technique was employed, and confusion matrices were constructed. Sensitivity and specificity were calculated to assess the agreement between research-grade and consumer wearables with the criterion polysomnography. For sleep versus wake classification, LSTM outperformed logistic regression and random forest with accuracy ranging from 94.1to 95.1, sensitivity ranging from 94.9 to 95.9 across different devices, and specificity ranging from 84.5 to 89.6. The addition of HR improved the prediction of sleep stages but not binary sleep versus wake. LSTM is promising for predicting sleep and sleep staging from actigraphy data, and HR may improve sleep stage prediction.

Exact Sampling Of The Six-Vertex Model Using Coupling From The Past, Malaeka Amir

DePaul Discoveries

This paper aims to explore the six-vertex model through simulations designed to investigate the behavior of configurations under specific domain wall boundary conditions. To generate random configurations, we employ the Markov Chain Monte Carlo method while addressing the challenge of mixing times by utilizing the Coupling from the Past (CFTP) algorithm. Implemented in Python, our approach leverages CFTP to ensure exact sampling, avoiding the uncertainty of convergence in traditional Monte Carlo methods. We explore the monotonicity property within this framework and prove that it is only maintained by the steps of this algorithm for very particular values of the parameters.

Unified Hybrid Censoring Samples From Power Pratibha Distribution And Its Applications, Mahmoud Mansour, Hebatalla H. Mohammad Dr, Khalaf S. Sultan Prof.

Basic Science Engineering

This paper suggests an extensive inferential method for the Power Pratibha Distribution (PPD) under Unified Hybrid Censoring Schemes (UHCSs), since there is a growing interest in flexible models in both reliability and service operations. This work studies the PPD model using standard Maximum Likelihood Estimation methods and modern Bayesian approaches too. Using a complex architecture, UHCS simulates tests more closely to what is done in practice than by using more basic censoring schemes. Using analysis, the probability and statistical ranges are carefully calculated for the parameters. Tests demonstrate that Bayesian estimation gives better results than many other methods for estimation, …

Effects Of Fair Workweek Laws On Labor Market Outcomes, Joseph Pickens, Aaron Sojourner

Upjohn Institute Working Papers

This paper models fair workweek regulations that require employers to provide employees with (1) schedule predictability via advance notice of their work schedule and premium payments for short-notice changes, and (2) access to hours meaning they must offer open hours to existing employees before hiring new workers. We develop a theoretical model of employers’ responses to these provisions and their implications for employment. Guided by the model, we estimate the effects of recently-adopted fair workweek regulation in New York City’s fast-food sector using a synthetic difference-in-differences design. We find a null employment effect.

Dice Math And Probability, Warren Campbell

SEAS Faculty Publications

The manufacture of dice for tabletop games is a billion-dollar industry. In gaming circles and online forums, the concept of “cursed dice” is a popular topic. Dice are inherently unfair due to the difficulty of manufacturing them with perfect geometric tolerances and uniform material densities. A common method for testing dice fairness is the chi-square statistic, which is typically assumed to follow the chi-square distribution. However, this assumption is only asymptotically valid.

Exact distributions of the statistic can be computed for dice with few sides and a limited number of rolls—such as 2-sided (D2) and 4-sided (D4) dice—but the computational …

Syntax-Enhanced Boundary-Aware Named Entity Recognition Model, Chuanming Yu, Bin Deng, Zhengang Zhang

Journal of Scientific Information Research

[Purpose/significance] This study addresses the issue of inadequate perception of entity boundaries in traditional character-level modeling-based named entity recognition models by integrating syntax information containing entity boundary features into the task using a multi-head graph attention network with dense connections. This integration enhances the effectiveness of named entity recognition.

[Method/process] This study proposes a Syntax-enhanced Boundary-aware Named Entity Recognition Model (SynBNER), which utilizes BERT for text semantic representation and integrates syntax information using a dense-connected graph attention network. This integration incorporates implicit entity boundary information from syntax information into word representations, thereby enhancing the model's entity boundary perception capability.

[Result/conclusion] …

Advancing Statistical Methods For Multivariate And Network Meta-Analysis, Yifei Wang

Statistical Science Theses and Dissertations

Multivariate meta-analysis (MMA) and network meta-analysis (NMA) are essential tools for synthesizing evidence across multiple correlated outcomes and treatments. However, these tools face practical challenges, including outcome reporting bias (ORB), unreported within-study correlations, and computational burden. ORB can distort effect estimates in MMA, while missing within-study correlations in multivariate NMA may lead to biased conclusions. To address these challenges, this dissertation introduces two novel statistical methods. For MMA, we propose SemiMMA, a semiparametric and scalable approach that treats ORB as a missing-not-at-random problem and combines inverse propensity weighting (IPW) with the generalized method of moments (GMM). For multivariate NMA, we …