Other Statistics and Probability Commons^™

A Survey On The Use Of Plastic Versus Biodegradable Bottles For Drinking Water Packaging In The United Arab Emirates, Himadri Rajput, Munjed A. Maraqa, Fatima Zraydi, Lina A. Al Khatib, Noor Ameen, Rime Ben Elkaid, Safia S. Al Jaberi, Noura A. Alharbi, Reka Howard, Ashraf Aly Hassan

Department of Statistics: Faculty Publications

Due to intensive utilization and extensive production, plastic waste is becoming a serious threat to the environment and human health. The situation is even worse in countries such as the United Arab Emirates (UAE), where single-use plastic water bottles add to the load of plastic pollution. The main objective of this survey was to assess the extent of bottled water utilization by the UAE residents and their awareness of the environmental concerns arising from single-use plastic bottles. The aim was also to evaluate their willingness to shift towards using biodegradable plastic bottles. This study involved the feedback of 2589 respondents …

Seasonal Variation In Terrestrial Invertebrate Subsidies To Tropical Streams And Implications For The Feeding Ecology Of Hart’S Rivulus (Anablepsoides Hartii), David C. Owens, Thomas N. Heatherly, Kent M. Eskridge, Colden V. Baxter, Steven A. Thomas

Department of Statistics: Faculty Publications

Terrestrial invertebrates are important subsidies to fish diets, though their seasonal dynamics and importance to tropical stream consumers are particularly understudied. In this year-round study of terrestrial invertebrate input to two Trinidadian headwater streams with different forest canopy densities, we sought to (a) measure the mass and composition of terrestrial inputs with fall-in traps to evaluate the influences of seasonality, canopy cover, and rainfall intensity, and; (b) compare terrestrial and benthic prey importance to Anablepsoides hartii(Hart’s Rivulus), the dominant invertivorous fish in these streams, by concurrently measuring benthic and drifting invertebrate standing stocks and the volume and composition of …

Cranberry Polyphenols In Esophageal Cancer Inhibition: New Insights, Katherine M. Weh, Yun Zhang, Connor L. Howard, Amy B. Howell, Jennifer L. Clarke, Laura A. Kresty

Department of Statistics: Faculty Publications

Esophageal adenocarcinoma (EAC) is a cancer characterized by rapidly rising incidence and poor survival, resulting in the need for new prevention and treatment options. We utilized two cranberry polyphenol extracts, one proanthocyanidin enriched (C-PAC) and a combination of anthocyanins, flavonoids, and glycosides (AFG) to assess inhibitory mechanisms utilizing premalignant Barrett’s esophagus (BE) and EAC derived cell lines. We employed reverse phase protein arrays (RPPA) and Western blots to examine cancer-associated pathways and specific signaling cascades modulated by C-PAC or AFG. Viability results show that C-PAC is more potent than AFG at inducing cell death in BE and EAC cell lines. …

Genome-Wide Association Study Of Disease Resilience Traits From A Natural Polymicrobial Disease Challenge Model In Pigs Identifies The Importance Of The Major Histocompatibility Complex Region, Jian Cheng, Rohan Fernando, Hao Cheng, Stephen D. Kachman, Kyusang Lim, John C.S. Harding, Michael K. Dyck, Frederic Fortin, Graham S. Plastow, Piggen Canada Research Consortium, Jack C.M. Dekkers

Department of Statistics: Faculty Publications

Infectious diseases cause tremendous financial losses in the pork industry, emphasizing the importance of disease resilience, which is the ability of an animal to maintain performance under disease. Previously, a natural polymicrobial disease challenge model was established, in which pigs were challenged in the late nursery phase by multiple pathogens to maximize expression of genetic differences in disease resilience. Genetic analysis found that performance traits in this model, including growth rate, feed and water intake, and carcass traits, as well as clinical disease phenotypes, were heritable and could be selected for to increase disease resilience of pigs. The objectives of …

Kryging: Geostatistical Analysis Of Large-Scale Datasets Using Krylov Subspace Methods, Suman Majumder, Yawen Guan, Brian J. Reich, Arvind K. Saibaba

Department of Statistics: Faculty Publications

Analyzing massive spatial datasets using a Gaussian process model poses computational challenges. This is a problem prevailing heavily in applications such as environmental modeling, ecology, forestry and environmental health. We present a novel approximate inference methodology that uses profile likelihood and Krylov subspace methods to estimate the spatial covariance parameters and makes spatial predictions with uncertainty quantification for point-referenced spatial data. The proposed method, Kryging, applies for both observations on regular grid and irregularly-spaced observations, and for any Gaussian process with a stationary isotropic (and certain geometrically anisotropic) covariance function, including the popular Matérn covariance family. We make use of …

Expanding The Network Evaluation Toolkit: Combining Social Network Analysis & Qualitative Comparative Analysis, Debbie Gowensmith

Electronic Theses and Dissertations

Collective action networks are complex systems of interrelated individuals or groups that come together for a common social change purpose (Ernstson, 2011). Researchers have used social network analysis (SNA) to examine the relationship structures and characteristics of collective action networks. However, determining whether collective action networking produces outcomes has been challenging because networks are complex, affected by context, and produce interdependent data. I addressed these challenges by pairing SNA with qualitative comparative analysis (QCA), a configurational comparative method. Using QCA, researchers can tease out which conditions are necessary or sufficient to produce an outcome. I analyzed a collective action network …

Using The Fraction Of Missing Information (Fmi) In Selecting Auxiliary Variables To Impute Missingness In Confirmatory Factor Analysis (Cfa), Dareen Taha Alzahrani

Electronic Theses and Dissertations

This study aimed to investigate the effectiveness of using the fraction of missing information (FMI) to select auxiliary variables in imputing missing data in confirmatory factor analysis (CFA). This was done by conducting two studies (a simulation study and an empirical study). A Monte Carlo simulation technique was used to compare the performance and the effect of the restrictive strategy based on FMI and the inclusive strategy on parameter estimate bias and parameter estimate efficiency. The missing data mechanisms, missing data proportion, correlation strength between the analysis variables and auxiliary variables, and the inclusive and restrictive strategies were assessed in …

Development And Initial Validation Of An Acculturation Measure For Arab Expatriates In Qatar Using Mixed Methods, Sara Ali Ahmed Zikri

Electronic Theses and Dissertations

Prior research on acculturation has predominantly focused on specific immigrant and ethnic groups in the United States, Europe, and Asia, and there is a lack of publicly available scales that measure acculturation among expatriates in countries of the Gulf Cooperation Council. The purpose of this study was to use a mixed methods research design to develop a theoretical framework of acculturation for white-collar Arab expatriates in Qatar as well as a culturally appropriate self-report acculturation measure. The acculturation stories and experiences of white-collar Arab expatriates in Qatar were qualitatively explored and analyzed, and their findings were used to construct an …

Containing Compounding Container Congestion, Curtis Salinger

CMC Senior Theses

The Covid-19 pandemic caused major disruptions throughout the container shipping supply chain. Professor Dongping Song of Liverpool University wrote a paper discussing the logistical vulnerabilities in the supply chain, including the issue of congestion in ports. This paper examines the Port of Los Angeles from 2018-2021 as it relates to Song’s paper to see how its operations were impacted during the Covid-19 timeframe. It is found that labor shortages, chassis shortages, and change in trade behavior each contributed to the congestion. Unfortunately, the implemented policies were insufficient to bolster the port against sustained challenges and congestion continues to worsen.

Statistical Theory For Specialized Linear Regression Adjustment Methods Compared To Multiple Linear Regression In The Presence And Absence Of Interaction Effects, Leon Su

Theses and Dissertations--Statistics

When building models to investigate outcomes and variables of interest, researchers often want to adjust for other variables. There is a variety of ways that these adjustments are performed. In this work, we will consider four approaches to adjustment utilized by researchers in various fields. We will compare the efficacy of these methods to what we call the ”true model method”, fitting a multiple linear regression model in which adjustment variables are model covariates. Our goal is to show that these adjustment methods have inferior performance to the true model method by comparing model parameter estimates, power, type I error, …

Role Of Inhibition And Spiking Variability In Ortho- And Retronasal Olfactory Processing, Michelle F. Craft

Theses and Dissertations

Odor perception is the impetus for important animal behaviors, most pertinently for feeding, but also for mating and communication. There are two predominate modes of odor processing: odors pass through the front of nose (ortho) while inhaling and sniffing, or through the rear (retro) during exhalation and while eating and drinking. Despite the importance of olfaction for an animal’s well-being and specifically that ortho and retro naturally occur, it is unknown whether the modality (ortho versus retro) is transmitted to cortical brain regions, which could significantly instruct how odors are processed. Prior imaging studies show different …

Fact Sheet, Yale University Office Of Institutional Research

Fact Sheets

No abstract provided.

Realtime Event Detection In Sports Sensor Data With Machine Learning, Mallory Cashman

Honors Theses and Capstones

Machine learning models can be trained to classify time series based sports motion data, without reliance on assumptions about the capabilities of the users or sensors. This can be applied to predict the count of occurrences of an event in a time period. The experiment for this research uses lacrosse data, collected in partnership with SPAITR - a UNH undergraduate startup developing motion tracking devices for lacrosse. Decision Tree and Support Vector Machine (SVM) models are trained and perform with high success rates. These models improve upon previous work in human motion event detection and can be used a reference …

Maximum Likelihood Estimator Method To Estimate Flaw Parameters For Different Glass Types, Nabhajit Goswami

Dissertations, Master's Theses and Master's Reports

Glass is commonly used in architectural applications, such as windows and in-fill panels and structural applications, such as beams and staircases. Despite the popularity of structural glass use in buildings, an engineering design standard to determine the required component or member strength for design loads does not exist. Glass is a brittle material that lacks a well-defined yield or ultimate stress, unlike ductile materials. The traditional engineering methods used to design a ductile material cannot be used to design a glass component. Glass fails in tension primarily due to the presence of microscopic flaws present on the surface that acts …

Nidus Idearum. Scilogs, Ix: Neutrosophia Perennis, Florentin Smarandache

Branch Mathematics and Statistics Faculty and Staff Publications

In this ninth book of scilogs collected from my nest of ideas, one may find new and old questions and solutions, – in email messages to research colleagues, or replies, and personal notes, some handwritten on the planes to, and from international conferences, about topics on Neutrosophy and its applications, such as: Neutrosophic Bipolar Set, Linguistic Neutrosophic Set, Neutrosophic Resonance Frequency, n-ary HyperAlgebra, n-ary NeutroHyperAlgebra, n-ary AntiHyperAlgebra, Plithogenic Crisp Graph, Plithogenic Fuzzy Graph, Plithogenic Intuitionistic Fuzzy Graph, Plithogenic Neutrosophic Graph, Plithogenic Real Number Graph, Plithogenic Complex Number Graph, Plithogenic Neutrosophic Number Graph, and many more.

Exchanging ideas with: Tareq Al-Shami, …

Examining The Credibility Of Story-Based Causal Methodologies, Megan E. Kauffmann

Electronic Theses and Dissertations

The purpose of this study was to explore how evaluators justify using story-based methodologies when examining causality. The two primary research questions of the study included: 1) what arguments are made by evaluators to justify the credibility of story-based causal methodologies to evaluation stakeholders; and 2) from the perspective of evaluators, how do contextual factors influence whether story-based causal methodologies are perceived as credible by evaluation stakeholders? A case study was conducted to examine the cases of four evaluators who had experience implementing a story-based methodology in an evaluation. Data collection procedures included two interviews with each participant and a …

Approximate Likelihood Based Estimations For Joint Models With Intractable Likelihoods, Karl Stessy M. Bisselou

Theses & Dissertations

This dissertation focuses on the development of approximation approaches for the joint modeling (JM) of repeated measures data and time-to-event data in the presence of analytically or numerically intractable likelihoods. Current likelihood-based inferences for JMs show several limitations including (i) intractability of integrals during marginal likelihood derivations due to the complexity in computations, and (ii) the large number of nuisance parameters (unobserved) posing a problem with convergence. The h-likelihood (HL) and synthetic likelihood (SL) are two computationally efficient estimation approaches that overcome these challenges.

In the presence of extremely high censoring rates, the HL can produce bias parameter estimates. We …

Confidence Interval For The Mean Of A Beta Distribution, Sean Rangel

Electronic Theses and Dissertations

Statistical inference for the mean of a beta distribution has become increasingly popular in various fields of academic research. In this study, we developed a novel statistical model from likelihood-based techniques to evaluate various confidence interval techniques for the mean of a beta distribution. Simulation studies will be implemented to compare the performance of the confidence intervals. In addition to the development and study involving confidence intervals, we will also apply the confidence intervals to real biological data that was gathered by the Department of Biology at Stephen F. Austin State University and provide recommendations on the best practice.

Faculty Versus Student Repeatability On Evaluating Translucency Of The Anterior Dentition, James L. Sheets, David B. Marx, Nina Ariani, Valentim A. R. Barão, Alvin G. Wee

Department of Statistics: Faculty Publications

The objective was to compare the repeatability between dental faculty, whose clinical practice was primarily restorative dentistry, and final year dental students in categorizing the inherent translucency of images selected at random using either a 3- or 7-point scale (translucent to opaque). Digital images of anterior dentition were randomly selected based on inherent translucency. Thirty images (five were repeated) were randomized and categorized by 20 dental students and 20 faculty on their inherent translucency. Statistical analysis was performed using an F test for analysis of variance at 95% confidence interval. A covariance parameter estimate (CPE) was accomplished to compare the …

Incorporating Molecular Markers And Causal Structure Among Traits Using A Smith-Hazel Index And Structural Equation Models, Juan Valente Hidalgo-Contreras, Josafhat Salinas-Ruiz, Kent M. Eskridge, Stephen P. Baenziger

Department of Statistics: Faculty Publications

The goal in breeding programs is to choose candidates that produce offspring with the best phenotypes. In conventional selection, the best candidate is selected with high genotypic values (unobserved), in the assumption that this is related to the observed phenotypic values for several traits. Multi-trait selection indices are used to identify superior genotypes when a number of traits are to be considered simultaneously. Often, the causal relationship among the traits is well known. Structural equation models (SEM) have been used to describe the causal relationships among variables in many biological systems. We present a method for multi-trait genomic selection that …

Posterior Propriety Of An Objective Prior For Generalized Hierarchical Normal Linear Models, Cong Lin, Dongchu Sun, Chengyuan Song

Department of Statistics: Faculty Publications

Bayesian Hierarchical models has been widely used in modern statistical application. To deal with the data having complex structures, we propose a generalized hierarchical normal linear (GHNL) model which accommodates arbitrarily many levels, usual design matrices and ‘vanilla’ covariance matrices. Objective hyperpriors can be employed for the GHNL model to express ignorance or match frequentist properties, yet the common objective Bayesian approaches are infeasible or fraught with danger in hierarchical modelling. To tackle this issue, [Berger, J., Sun, D., & Song, C. (2020b). An objective prior for hyperparameters in normal hierarchical models. Journal of Multivariate Analysis, 178, 104606. https://doi.org/10.1016/j.jmva.2020.104606] …

Novel Statistical Modeling Methods For Traffic Video Analysis, Hang Shi

Dissertations

Video analysis is an active and rapidly expanding research area in computer vision and artificial intelligence due to its broad applications in modern society. Many methods have been proposed to analyze the videos, but many challenging factors remain untackled. In this dissertation, four statistical modeling methods are proposed to address some challenging traffic video analysis problems under adverse illumination and weather conditions.

First, a new foreground detection method is presented to detect the foreground objects in videos. A novel Global Foreground Modeling (GFM) method, which estimates a global probability density function for the foreground and applies the Bayes decision rule …

Factors Influencing Student Outcomes In A Large, Online Simulation-Based Introductory Statistics Course, Ella M. Burnham

Department of Statistics: Dissertations, Theses, and Student Research

The demand for statistical knowledge and skills is growing in many disciplines, so more students are enrolling in introductory statistics courses (Blair, Kirkman, & Maxwell, 2018). At the same time, institutions are seeking course delivery methods that allow for greater flexibility for students, especially following the onset of the COVID-19 pandemic; therefore, there is more interest in the development and delivery of online introductory statistics courses.

To address this, I collaboratively designed an online introductory statistics course which focuses on simulation-based inference for the University of Nebraska-Lincoln. The course design was informed by the Community of Inquiry framework (Garrison, Anderson, …

Prediction Intervals: The Effects And Identification Of Sparse Regions For Nonparametric Regression Methods, Jackson Faires

Electronic Theses and Dissertations

In this work, we provide an overview of different nonparametric methods for prediction interval estimation and investigate how well they perform when making predictions in sparse regions of the predictor space. This sparsity is an extension to the more common concept of extrapolation in linear regression settings. Using simulation studies, we show that coverage probabilities using prediction intervals from quantile k-nearest neighbors and quantile random forest can be biased to low or too high from the nominal level under various situations of sparsity. We also introduce a test that can be used to see if a new data point lies …

Fully Bayesian Analysis Of Relevance Vector Machine Classification With Probit Link Function For Imbalanced Data Problem, Wenyang Wang, Dongchu Sun, Peng Shao, Haibo Kuang, Cong Sui

Department of Statistics: Faculty Publications

The original RVM classification model uses the logistic link function to build the likelihood function making the model hard to be conducted since the posterior of the weight parameter has no closed-form solution. This article proposes the probit link function approach instead of the logistic one for the likelihood function in the RVM classification model, namely PRVM (RVM with the probit link function). We show that the posterior of the weight parameter in PRVM follows the Multivariate Normal distribution and achieves a closed-form solution. A latent variable is needed in our algorithms to simplify the Bayesian computation greatly, and its …

Application Of Randomness In Finance, Jose Sanchez, Daanial Ahmad, Satyanand Singh

Publications and Research

Brownian Motion which is also considered to be a Wiener process and can be thought of as a random walk. In our project we had briefly discussed the fluctuations of financial indices and related it to Brownian Motion and the modeling of Stock prices.

The Fundamental Limit Theorem Of Countable Markov Chains, Nathanael Gentry

Senior Honors Theses

In 1906, the Russian probabilist A.A. Markov proved that the independence of a sequence of random variables is not a necessary condition for a law of large numbers to exist on that sequence. Markov's sequences -- today known as Markov chains -- touch several deep results in dynamical systems theory and have found wide application in bibliometrics, linguistics, artificial intelligence, and statistical mechanics. After developing the appropriate background, we prove a modern formulation of the law of large numbers (fundamental theorem) for simple countable Markov chains and develop an elementary notion of ergodicity. Then, we apply these chain convergence results …

Does Defense Actually Win Championships? Using Statistics To Examine One Of The Greatest Stereotypes In Sports, Thomas Burkett

Senior Theses

A common saying in sports is that “defense wins championships.” However, the past decade of play in the modern NBA has seen a rise and focus in offensive efficiency and 3-pointers. This thesis tests whether defense can truly predict a championship winning team in today’s NBA through two-sample hypothesis testing and multiple logistic regression models. The results found that both defensive and offensive statistics were significant predictors of championship teams, meaning that a balanced team, rather than one specialized in defense alone, is a more accurate predictor of championship success.

Adventuring Into Complexity By Exploring Data: From Complicity To Sustainability, Tim Lutz

Northeast Journal of Complex Systems (NEJCS)

Problems of sustainability are typically represented by major present-day challenges such as climate change, biodiversity loss, and environmental and social injustice. Framed this way, sustainable lives and societies depend on finding solutions to each problem. From another perspective, there is only one problem behind them all, stated by Gregory Bateson as: “…the difference between how nature works and the way people think,” and complexity provides a way to define and approach this problem. I extend Edgar Morin’s conceptions of restricted and general complexity into pedagogy to address problems of simplicity and reductionist teaching. The proposed pedagogy is based on long …

Entropic Dynamics Of Networks, Felipe Xavier Costa, Pedro Pessoa

Northeast Journal of Complex Systems (NEJCS)

Here we present the entropic dynamics formalism for networks. That is, a framework for the dynamics of graphs meant to represent a network derived from the principle of maximum entropy and the rate of transition is obtained taking into account the natural information geometry of probability distributions. We apply this framework to the Gibbs distribution of random graphs obtained with constraints on the node connectivity. The information geometry for this graph ensemble is calculated and the dynamical process is obtained as a diffusion equation. We compare the steady state of this dynamics to degree distributions found on real-world networks.