Statistics and Probability Commons^™

Does A Name Make A Difference? Teaching Random Selection In The Classroom, Venessa Singhroy, Rommel Robertson, Kostas Stroumbakis

Numeracy

In Statistics education, it is crucial to emphasize the foundational significance of random selection, which underpins statistical methodologies and ensures unbiased representation of populations in samples. However, students often struggle to grasp the concept’s complexities, leading to challenges in applying random selection methods effectively. This paper examines the gap between students’ theoretical understanding of randomness and their practical application of this concept. Using an Explanatory Sequential Design, this study presents an instructional activity aimed at teaching the concept of randomness in the selection process and proposes modifications to enhance student comprehension. The activity, implemented in undergraduate Statistics and Psychology courses, …

A Latent Class Assessment Of Healthcare Access Factors And Disparities In Breast Cancer Care Timeliness, Matthew Dunn, Didong Li, Marc Emerson, Caroline Thompson, Hazel Nichols, Sarah Van Alsten, Mya Roberson, Stephanie Wheeler, Lisa Carey, Terry Hyslop, Jennifer Elston Lafata, Melissa Troester

Kimmel Cancer Center Faculty Papers

BACKGROUND: Delays in breast cancer diagnosis and treatment lead to worse survival and quality of life. Racial disparities in care timeliness have been reported, but few studies have examined access at multiple points along the care continuum (diagnosis, treatment initiation, treatment duration, and genomic testing).

METHODS AND FINDINGS: The Carolina Breast Cancer Study (CBCS) Phase 3 is a population-based, case-only cohort (n = 2,998, 50% black) of patients with invasive breast cancer diagnoses (2008 to 2013). We used latent class analysis (LCA) to group participants based on patterns of factors within 3 separate domains: socioeconomic status ("SES"), "care barriers," and …

Multiple And Nonexistence Of Positive Solutions For A Class Of Fractional Differential Equations With P-Laplacian Operator, Haoran Zhang, Zhaocai Hao, Martin Bohner

Mathematics and Statistics Faculty Research & Creative Works

Research about multiple positive solutions for fractional differential equations is very important. Based on some outstanding results reported in this field, this paper continues the focus on this topic. By using the properties of the Green function and generalized Avery–Henderson fixed point theorem, we derive three positive solutions of a class of fractional differential equations with a p-Laplacian operator. We also study the nonexistence of positive solutions to the eigenvalue problem of the equation. Three examples are given to illustrate our main result.

(R2101) Analysis Of Map/Ph/1 Queueing Inventory System With Two Commodity, Working Vacation, (S, S) Replenishment Policy, Essential And Optional Repair, G. Ayyappan, N. Arulmozhi

Applications and Applied Mathematics: An International Journal (AAM)

We examine a queueing inventory model with single server which can offer two types of inventory items: main item (commodity I) and complementary item (commodity II). We assume both commodities have a finite capacity Si, i = 1, 2. Customers reach the system by following the Markovian arrival process (MAP). The service times are considered to be phase-type (PH) distribution. We have considered no customer in the system, even inventory level is positive; the server will start the working vacation, and any customer that arrives during working vacation, the server provides slow service. If an item is not available, the …

(R2095) On Discrete Hypoexponential Distribution And Integer-Valued Autoregressive Process, K. Krishnakumari, Dais George

Applications and Applied Mathematics: An International Journal (AAM)

Discrete distributions provide a substantive contribution in modeling real world frame work. Though a lot of discrete distributions are available in literature, they are inappropriate to model many practical situations. The conventional discrete distributions like geometric, Negative Binomial and Poisson have limited applications in modeling count and lifetime data. This paper introduces a new two parameter discrete distribution namely, discrete hypoexponential distribution by discretizing the well known hypoexponential distribution. Various distributional and structural properties of the proposed distribution are studied. We introduce a first order auto regressive process with the newly proposed distribution as marginal and study the properties. Depending …

Striking A Balance: Market Shock & Responses In Automotive Components Manufacturing, Emma Lane Mcgahey

All Theses

This thesis examines the effects of extreme market shocks on supply chain dynamics within the automotive industry. Through an analysis of demand data from an automotive manufacturer to its component suppliers (January 2018 to May 2024), the study investigates the relationship between market shocks and supply chain responses, providing insights into how auto components inventory management handles downstream responses to market shocks. With supporting public data—from FRED, BLS, and the U.S. Census Bureau resources—we explore two primary relationships: the impact of market shocks on the Average Standard Deviation of Demand (SDO) and the effect of demand variability on expedited pricing …

Optimization Of Markov Chain Modeling In Predicting College Student Retention, Kien Nguyen

Journal of Global Education and Research

College student retention is one of the most important metrics in higher education. With institutions across the US facing decreasing enrollment, developing a reliable retention prediction method is crucial. In recent years, the use of the Markov chain model in forecasting student enrollment and progression has become more common, but there is little work on its application in student retention. One key factor in determining this model's effectiveness is what parameters should be used in the student population’s segmentation or grouping. This study presents a rigorous algorithm, coupled with a prediction model, capable of selecting parameters that provide the most …

A Strategic Insight Into The Market For Carbon Management Capacity, Mahelet G. Fikru, Ting Shen, Jennifer Brodmann, Hongyan Ma

Economics Faculty Research & Creative Works

This study presents the market for carbon management capacity via carbon capture, utilization, and storage technologies, identifying demand and supply forces, as well as clarifying the potential impact of market and non-market-based shocks on technology developers versus adopters. The paper addresses a prevailing gap in market analysis, introducing a microeconomic framework and unique dataset to identify key players, market forces, and policy incentives shaping the carbon capture, utilization, and storage landscape. The analysis equips industry stakeholders, policymakers, and investors with valuable insights regarding (1) leaders in the design, development, and manufacture of carbon capture, utilization, and storage technologies (supply), (2) …

D-Optimal Joint Best Linear Unbiased Predictors In Progressively Type-Ii Ordered Statistics, Tamim Alam

Open Access Theses & Dissertations

Reliability and life-testing experiments play a crucial role in understanding the longevity and performance of systems and components, particularly in high-stakes applications such as engineering, manufacturing, and quality control. In this thesis, we focus on the prediction of future unobserved failure times by employing joint predictors based on progressively Type-II censored data obtained from such life-testing experiments. Specifically, we derive explicit analytical expressions for the joint best linear unbiased predictors (BLUPs) of two future order statistics under the D-optimality criterion. The derivation involves minimizing the determinant of the variance-covariance matrix of the predictors within the context of progressively Type-II censored …

Investigating Nekton Response To Changing Salinities In The Mississippi Sound: An Experimental And Statistical Approach, Adam Murray

Master's Theses

The Mississippi Sound provides nursery habitats for many coastal species and is recognized for its commercial fisheries. Previous freshening events linked to the Bonnet Carré Spillway, a Mississippi River flood diversion structure, proved catastrophic for oyster populations in the Mississippi Sound, but effects on mobile fish and shellfish species are not well-defined. The planned Mid-Breton Sediment Diversion (MBSD), an initiative to combat wetland loss, is also forecasted to lower salinities in the region, increasing the need to better understand responses of commercially and ecologically important species to freshening events. The objective of this study was to characterize effects of salinity …

Quantile Regression And Change Point Analysis Of Remote Patient Monitoring Data From Cardiomems Hf System, Shanshan Jia

All Dissertations

Quantile regression provides a sophisticated analytical approach for clinical data, offering deeper insights into patient variability and risk assessment than conventional regression techniques. This study delves into the mathematical underpinnings of quantile regression, highlighting its advantages over ordinary least squares (OLS) regression, particularly in the context of predicting diastolic pulmonary artery pressure (PAP). We explore how this method can be applied to guide clinical interventions more effectively. By leveraging quantile regression’s ability to model different parts of the outcome distribution, we demonstrate its potential to enhance patient care through improved identification of high-risk individuals and the development of more personalized …

(R2098) Dynamic Analysis Of Stochastic Leslie-Gower Biological Predator-Prey Model With Prey Cannibalism, Sada Nand Prasad, Itendra Kumar Universiry Of Delhi, India, Pawan Kumar

Applications and Applied Mathematics: An International Journal (AAM)

In this paper, we study the dynamical analysis of a stochastic Leslie–Gower biological predator– prey model. Earlier, the Leslie–Gower model was studied in the context of biological systems, including cases involving cannibalism. In our model, we investigate the dynamic properties of a stochastic Leslie–Gower predator–prey ecological system using the stability of invariant measures on invariant sets, where the invariant measures are shown to be ergodic. We also conduct a threshold analysis to study the stochastic persistence and extinction of species. Stochastic bifurcation is also examined. The theoretical results are supported by numerical simulations and examples. Intra-species competition is considered and …

(R2116) The Novel Garima Distribution Properties And Nuclear Data Application, Murat Aygün, Ayşe Metin Karakaş

Applications and Applied Mathematics: An International Journal (AAM)

This research introduces a new two-parameter Marshall-Olkin Garima distribution model. The novel model has many sub-models that are useful in modeling real-life data, such as the extended Garima distribution, exponentiated Garima distribution, exponential distribution, Lindley distribution, Kumaraswamy Garima distribution, and normal distribution. The proposed model demonstrates a high level of suitability in modeling both reliability and survival data. It is flexible in accommodating various failures. The quantile function, density shapes, hazard rate functions, and order statistics are a few of the statistical features that have been explored. Maximum likelihood estimation methods were employed to estimate the parameters. Using five data …

(R2117) Cost Optimization Of Queueing System With Differentiated Vacations And Reneging Of Customers, Poonam Gupta, Rajni Gupta

Applications and Applied Mathematics: An International Journal (AAM)

This manuscript deals with an infinite-capacity queueing system under multiple differentiated working vacations and customers’ impatience. The first vacation is assumed to be a working vacation where the server, instead of being idle, serves the customers at a lower rate. In contrast, the second one is considered a non-working vacation of a different duration. The customers may leave the system at any time due to long delays in service during vacations but, via some convincing mechanisms, they are retained in the system. The operating characteristics of the system are obtained in a steady state. The results obtained are illustrated numerically …

Statistics With Actuarial Emphasis, Zachariah Yonker

Honors Projects

This report presents the findings of a survey conducted among members of the Grand Valley State University Dungeons & Dragons Club, focusing on member age, gender, habits, and game preferences. The survey, administered during the Fall 2024 semester, received 31 responses, with 30 being completed and 1 partially finished, representing approximately 25% of active club members. Key findings via the Statistical Analysis System (SAS) include a diverse range of preferences and opinions about Dungeons & Dragons classes, as well as a consensus on the most difficult challenge of the activity in addition to favorite aspects of Dungeons & Dragons.

Quantifying The Impact Of Rain-On-Snow Induced Flooding In The Western United States, Emma M. Watts

All Graduate Theses and Dissertations, Fall 2023 to Present

Serious flooding can happen when rain falls on snow, which we call a rain-on-snow (ROS) event. Increasing our understanding of the behavior of floods resulting from ROS events can help us design better systems to manage flood water and prevent it from causing damage. This thesis explores how ROS events affect streamflow in the Western United States by examining the weather conditions that precede a streamflow surge. We classify stream surges as ROS or non-ROS induced based on these weather conditions, which helps us separate floods caused by ROS events from those caused by other factors. By comparing these different …

Variable Selection In Distance Metric Learning And Triplet Constraints For Deep Learning Based Ordinal Classification, James D. Clothier

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

The purpose of this research is to augment linear and kernelized ordinal distance metric learning (L/KODML) techniques with a proposed variable selection methodology that integrates the Sequential Multi-Response Feature Selection (SMuRFS) algorithm. Additionally, we aim to embed ordinal triplet constraints into a deep learning architecture, and to propose a general framework for deep learning-based ordinal classification. A variety of simulation studies and real data experiments were conducted to evaluate the various methodologies. For the distance metric learning and variable selection, results showed that the integration of SMuRFS performed effective variable selection and improved prediction accuracy. For the triplet constraints, incorporating …

Performance Of Acoustic Telemetry And Space Use Of Pallid Sturgeon In The Lower Platte River, Nebraska, Christopher F. Pullano

School of Natural Resources: Dissertations, Theses, and Student Research

Pallid Sturgeon (Scaphirhynchus albus) are centenarian, potamodromous, rheophiles that historically occupied the Missouri River and Mississippi River basins. Listed on the U.S. Endangered Species Act in 1990, population declines are attributed to habitat fragmentation and degradation, as well as overharvest, and hybridization. A knowledge gap exists regarding the extent to which tributaries facilitate key life stages for Pallid Sturgeon. This study evaluated the capacity of acoustic telemetry to monitor the movements of Pallid Sturgeon in a shallow, braided tributary to the Missouri River. The specific objectives were to (1) evaluate the environmental variables influencing the performance of acoustic …

Enhancing Predictive Accuracy In Noisy Data: A Robust Gradient Boosting Approach, Gabriela Alexa Acuna

Open Access Theses & Dissertations

This thesis aims to enhance the gradient boosting technique, a well-known machine learning method, in a regression setup. Gradient boosting techniques implement sequential weak learners, in this case, shallow trees that contribute to the model with a small percentage. The traditional gradient boosting approach uses regression trees that choose thresholds based on minimizing variance and makes predictions based on the mean of the target variable for the observations contained within each node. The residual sum of squares (RSS) and mean metrics are sensitive to outliers, which makes the model's predictions less robust. Outliers influence the predictions, resulting in a model …

Bayesian Lasso Regularized Quantile Regression And Its Applications, Priscilla Kissi-Appiah

Theses and Dissertations

Since the pioneering work of (Koenker and Bassett Jr 1978), quantile regression has been a popular regression technique that helps researchers investigate a whole distribution of the response variable. In addition, due to the quantile check loss function, it is robust against outliers and heavy-tailed distributions of the response variable and can provide a more comprehensive picture of modeling via exploring the conditional quantiles of the response variable. In this research, we study the lasso regularized quantile regression from a Bayesian perspective. We develop an efficient sampling algorithm to generate posterior samplings for making posterior inference by using a location-scale …

Comparison Of Statistical And Machine Learning Genomic Prediction Methods In Plant Breeding: Case Studies In Maize And Soybean, Igor Kuivjogi Fernandes

Graduate Theses and Dissertations

Plant breeding is essential to increase genetic gain and food production worldwide. This study was conducted to evaluate new ways to use machine learning (ML) to tackle plant breeding challenges, where two ideas were tested — the first chapter focuses on how to combine genetic and environmental data using ML to improve the prediction of maize grain yield in multi-environment trials, while the second chapter centers on how to couple feature selection of molecular markers with ML to enhance prediction of yield in soybean, and, in both cases, ML approaches were compared to well-established statistical methods greatly adopted by the …

Learning De-Biased Representations For Remote-Sensing Imagery, Zichen Tian, Zhaozheng Chen, Qianru Sun

Research Collection School Of Computing and Information Systems

Remote sensing (RS) imagery, requiring specialized satellites to collect and being difficult to annotate, suffers from data scarcity and class imbalance in certain spectrums. Due to data scarcity, training any large-scale RS models from scratch is unrealistic, and the alternative is to transfer pre-trained models by fine-tuning or a more data-efficient method LoRA. Due to class imbalance, transferred models exhibit strong bias, where features of the major class dominate over those of the minor class. In this paper, we propose debLoRA---a generic training approach that works with any LoRA variants to yield debiased features. It is an unsupervised learning approach …

Policies And Price Controls On The Research And Development Of Orphan Drugs In The United States And The European Union, Bena Pearl Filipczak Smith

Master's Theses

There is substantive literature surrounding the impact of price controls on the research and development (R&D) of new pharmaceutical products. The European Union (EU) and United States (US) are often studied in contrast to examine the influence of price controls as the US has fewer pharmaceutical price controls than the EU.

We find moderate evidence that the US spent more on annual domestic pharmaceutical R&D than the EU between 2004 and 2021, on average, before and after adjusting for GDP growth per capita and year. We find strong evidence that the US increased annual domestic R&D spending at a faster …

Using Neighborhood Information To Improve Fiber Direction Estimation From Neuroimaging Data, Anjan Mandal

UNLV Theses, Dissertations, Professional Papers, and Capstones

Accurately estimating neuronal fiber directions is crucial in neuroimaging analysis. The Ball-and-Stick Model (BSM), introduced by Behrens et al. (2003), and widely used in software tools like FSL, remains one of the most popular models for this purpose. BSM analyzes each voxel individually, based solely on its signal information.In this dissertation, we propose modifications to BSM, that incorporate signal information from neighboring voxels in the estimation process, potentially improving the accuracy of the estimates. Additionally, within the estimation process, we introduce two novel proposal distributions to enhance the efficiency of the Markov chain Monte Carlo sampling procedure on the simplex …

Decoding Neural Networks: An Information-Theoretic Guide To Interpretability, Error Analysis And Efficiency, Mackenzie J. Meni

Theses and Dissertations

This dissertation addresses critical challenges in neural network design by leveraging entropy-based techniques to improve model efficiency, interpretability, and bias reduction. Focusing on the unique demands of computer vision applications, particularly object detection and classification for real-time systems, this work introduces a series of innovative methods centered on information theory. At the core of these methods is the Probabilistic Explanations of Entropic Knowledge (PEEK) framework, a tool developed to analyze and visualize entropy distributions across feature maps. PEEK offers insights into information flow within neural networks, making it possible to pinpoint layers that contribute meaningfully to decision-making or identify those …

Impact Of Snow Accumulation On Structural Integrity: Present And Future Perspectives, Kenneth K. Pomeyie

All Graduate Theses and Dissertations, Fall 2023 to Present

In the United States, accommodating the weight of accumulated snow on buildings is a crucial consideration in building design. Engineers are tasked with determining the design snow load, which is defined as the weight of accumulated snow that a structure should withstand to limit the risk of building collapse to an acceptably low level. Typically, this process involves analyzing historical data of the annual maximum snow accumulations for each snow season. However, accurately assessing these design snow loads entails navigating through a series of statistical challenges. This dissertation, composed of three papers, is dedicated to addressing these statistical hurdles in …

Early Detection Of Risk Factor For Suicidal Ideation Among Senior High School Students In Jakarta: Updated Measurement, Nova R. Yusuf, Sabarinah Prasetyo, Byron J. Good

Kesmas

The key strategy to address suicide in adolescents is school-based suicidal prevention by adapting a screening instrument to the local culture and policymakers’ perception of suicide. This study aimed to develop an instrument for the early detection of risk for suicidal ideation and identify influential risk factors for suicidal ideation among high school students in Jakarta, Indonesia. This study was conducted in 2018 with a mixed-method design (quantitative and qualitative approaches). It was found that 5% of students had suicidal ideation in July–November 2018, and 13.8% had a high-risk factor for suicidal ideation. The instrument developed in this study consisted …

Telemedicine Adoption In Developing Economies: A Systematic Review On The Enablers And Barriers, Zaidbren Macabato, Lemuel Clark Velasco, Art Brian Escabarte, Mae-Lanie Ong Poblete, Armando Isla Jr., Rentor Cafino, Sarah Lizette Aquino-Cafino, Frevy Teofilo-Orencia

Kesmas

Telemedicine’s adoption has been effective in certain contexts despite being controversial in certain settings because of its tendency to cause misdiagnosis and concerns about data privacy. This study aimed to synthesize the research findings on the factors leading to the adoption of telemedicine among developing economies. The study utilized Preferred Reporting Items for Systematic Reviews and Meta-Analysis methodology to analyze 27 related literature and the Unified Theory of Acceptance and Use of Technology to map out the factors considered enablers and barriers in adopting telemedicine. Results showed that performance expectancy, effort expectancy, social influence, and facilitating conditions were significant predictors. …

Spatial Pattern And Differential Expression Analysis With Spatial Transcriptomic Data, Fei Qin, Xizhi Luo, Bo Cai Ph.D., Feifei Xiao, Guoshuai Cai

Faculty Publications

The emergence of spatial transcriptomic technologies has opened new avenues for investigating gene activities while preserving the spatial context of tissues. Utilizing data generated by such technologies, the identification of spatially variable (SV) genes is an essential step in exploring tissue landscapes and biological processes. Particularly in typical experimental designs, such as case-control or longitudinal studies, identifying SV genes between groups is crucial for discovering significant biomarkers or developing targeted therapies for diseases. However, current methods available for analyzing spatial transcriptomic data are still in their infancy, and none of the existing methods are capable of identifying SV genes between …

Pediatric Renal Cell Carcinoma (Prcc) Subpopulation Environmental Differentials In Survival Disadvantage Of Black/African American Children In The United States: Large-Cohort Evidence, Laurens Holmes, Phatismo Masire, Arieanna Eaton, Robert Mason, Mackenzie Holmes, Justin William, Maura Poleon, Michael Enwere

College of Population Health Faculty Papers

OBJECTIVE: Renal cell carcinoma (RCC) is a rare but severe and aggressive pediatric malignancy. While incidence is uncommon, survival is relatively low with respect to acute lymphocytic leukemia (ALL), AML, lymphoma, ependymoma, glioblastoma, and Wilms Tumor. The pediatric renal cell carcinoma (pRCC) incidence, cumulative incidence (period prevalence), and mortality vary by health disparities' indicators, namely sex, race, ethnicity, age at tumor diagnosis, and social determinants of health (SDHs) as well as Epigenomic Determinants of Health (EDHs). However, studies are unavailable on some pRCC risk determinants, such as area of residence and socio-economic status (SES). The current study aimed at assessing …