Statistics and Probability Commons^™

Simultaneous Application Of Multiple Process Control Rules, Tran B. Ngo

Electronic Theses and Dissertations

Statistical Process Control (SPC) charts are tools used in quality control to monitor and analyze the stability of a process over time. This study evaluates the effectiveness of eight individual Western Electric rules, also known as WECO rules, and the various combinations of these rules with Shewhart rule (or WECO rule 1) to SPC charts. As more rules are added to a process control scheme with Rule 1, there is a trade-off: a higher false out-of-control signal rate but an increase in sensitivity, that is the ability of a specified process control scheme to capture a true out-of-control signal. This …

Detection Of Activity Cliffs Produced By Anti-Cancer Drugs And An Algorithm For Reliable Predictions In Affected Areas, Sarah Josephine Aurit

Department of Statistics: Dissertations, Theses, and Student Research

An activity cliff (AC) occurs when drugs close in chemical space produce dissimilar biological results. We focus on developing an inferential procedure to detect the presence of ACs in a chemical landscape. If detected, we provide a distance-based procedure that can be used to identify regions of stability in the chemical landscape of interest and generate prediction with higher precision in those areas of stability. We conceptualize the chemical landscape as a spatial random field and use spatial models for prediction of efficacy for new drugs based on “distance” in chemical space. We argue that an AC manifests itself by …

Comparative Analysis Of Sequential And Non-Sequential Modeling Techniques For Ddos Attack Detection With Explainable Ai, Vincent Agbenyeavu

Theses and Dissertations

Cybersecurity is known today as one of the greatest challenges of the modern era. Among the various types of cyber-attacks that threaten our security, the Distributed Denial of Service (DDoS) attack is among some of the most common, effective, and well-recognized attack strategies. Since this form of attack is meant to disrupt the availability factor covertly, it can be detrimental to the targeted machines and difficult to discover. Because of that, there have been several approaches, as well as solutions that have been devised to detect it as accurately and efficiently as possible. In this study, four sequential data modeling …

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 …

A Multi-Modal Method For Synthetic Data Generation In Social Network Analysis, Hortencia Josefina Hernandez

Open Access Theses & Dissertations

Social network analysis (SNA) research is often rife with data collection pitfalls, frequently leading to incomplete and missing data. With the growing use of SNA-based research, researchers must address the challenge of missing data and synthetic data generation in these settings. Missing data occurs due to longitudinal non-response or lack of response to sensitive or difficult-to-answer questions. Synthetic data generation in SNA settings addresses the lack of representation that is often present in large-scale SNA studies. This dissertation investigates synthetic data generation methods to address these challenges and develops a novel algorithm that leverages information from multi-modal data, e.g., databases …

Simultaneous Selection Of Inflations And Variables In Multiple Inflations Poisson Model (Mip), John Koomson

Open Access Theses & Dissertations

Count data frequently arise in biomedical, economic, and social science research and are often characterized by structural excesses at specific count levels. To accommodate such patterns, Su et al. (2013), among others, introduced the Multiple-Inflation Poisson (MIP) model, which allows for multiple inflated counts within the distribution. However, two critical challenges remain in modeling such data: (i) identifying the true inflation points where excess counts occur, and (ii) selecting the relevant covariates that explain variation in the inflation and count process. This dissertation addresses these issues by advancing the MIP model through a novel methodology that enables the simultaneous selection …

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, …

Experimental Design And Analysis For Decision Making: Methodology And Applications, Yezhuo Li

All Dissertations

This dissertation develops and applies advanced statistical and optimization frameworks to enhance decision-making under uncertainty, particularly in engineering and manufacturing contexts. First, we introduce an approach for the optimal design of controlled experiments that accounts for observational covariates, enabling more precise and personalized decisions. Second, we explore the application of constrained Bayesian optimization, using Gaussian process surrogate models, to optimize composite cure processes, significantly reducing computational effort while maintaining high predictive accuracy. Building on this foundation, we extend Bayesian optimization to bivariate Gaussian process models that capture correlations between objective and constraint functions, offering new insights into multidimensional decision landscapes. …

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, …

A Novel Diastolic Doppler Index Less Affected By Aortic Arch Anomalies Co-Existing With Coarctation, Arash Ghorbannia, Andrew D. Spearman, Shahd Sawalhi, Ronald K. Woods, Mehdi Maadooliat, John F. Ladisa Jr.

Mathematical and Statistical Science Faculty Research and Publications

Severity assessment for coarctation of the aorta (CoA) is challenging due to concomitant morphological anomalies (complex CoA) and inaccurate Doppler-based indices. Promising diagnostic performance has been reported for the continuous flow pressure gradient (CFPG), but it has not been studied in complex CoA. Our objective was to characterize the effect of complex CoA and associated hemodynamics on CFPG in a clinical cohort. Retrospective analysis identified discrete juxtaductal (n = 25) and complex CoA (n = 43; transverse arch and/or isthmus hypoplasia) patients with arm-leg systolic blood pressure gradients (BPG) within 24 h of echocardiography for comparison to BPG …

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 …

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.

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 …

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 …

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 …

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 …

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 …

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, …