Statistics and Probability Commons^™

Session 6: Model-Based Clustering Analysis On The Spatial-Temporal And Intensity Patterns Of Tornadoes, Yana Melnykov, Yingying Zhang, Rong Zheng

SDSU Data Science Symposium

Tornadoes are one of the nature’s most violent windstorms that can occur all over the world except Antarctica. Previous scientific efforts were spent on studying this nature hazard from facets such as: genesis, dynamics, detection, forecasting, warning, measuring, and assessing. While we want to model the tornado datasets by using modern sophisticated statistical and computational techniques. The goal of the paper is developing novel finite mixture models and performing clustering analysis on the spatial-temporal and intensity patterns of the tornadoes. To analyze the tornado dataset, we firstly try a Gaussian distribution with the mean vector and variance-covariance matrix represented as …

Mapping Urban Form Into Local Climate Zones For The Continental Us From 1986–2020, Meng Qi, Chunxue Xu, Wenwen Zhang, Matthias Demuzere, Perry Hystad, Tianjun Lu, Peter James, Benjamin Bechtel, Steve Hankey

Earth and Environmental Sciences Faculty Publications

Urbanization has altered land surface properties driving changes in micro-climates. Urban form influences people’s activities, environmental exposures, and health. Developing detailed and unified longitudinal measures of urban form is essential to quantify these relationships. Local Climate Zones [LCZ] are a culturally-neutral urban form classification scheme. To date, longitudinal LCZ maps at large scales (i.e., national, continental, or global) are not available. We developed an approach to map LCZs for the continental US from 1986 to 2020 at 100 m spatial resolution. We developed lightweight contextual random forest models using a hybrid model development pipeline that leveraged crowdsourced and expert labeling …

Modeling Of Covid-19 Clinical Outcomes In Mexico: An Analysis Of Demographic, Clinical, And Chronic Disease Factors, Livia Clarete

Dissertations, Theses, and Capstone Projects

This study explores COVID-19 clinical outcomes in Mexico, focusing on demographic, clinical, and chronic disease variables to develop predictive models. In the binary classification task, the Ada Boost Classifier distinguishes survivors from non-survivors, with age, sex, ethnicity, and chronic medical conditions influencing outcomes. In multiclass classification, the Gradient Boosting Classifier categorizes patients into outcome groups.

Demographic variables, especially age, are crucial for predicting COVID-19 outcomes for both the binary and multiclass classification tasks. Clinical information about previous conditions, including chronic diseases, also holds relevance, especially diabetes, immunocompromise, and cardiovascular diseases. These insights inform public health measures and healthcare strategies, emphasizing …

A Causal Inference Approach For Spike Train Interactions, Zach Saccomano

Dissertations, Theses, and Capstone Projects

Since the 1960s, neuroscientists have worked on the problem of estimating synaptic properties, such as connectivity and strength, from simultaneously recorded spike trains. Recent years have seen renewed interest in the problem coinciding with rapid advances in experimental technologies, including an approximate exponential increase in the number of neurons that can be recorded in parallel and perturbation techniques such as optogenetics that can be used to calibrate and validate causal hypotheses about functional connectivity. This thesis presents a mathematical examination of synaptic inference from two perspectives: (1) using in vivo data and biophysical models, we ask in what cases the …

Making Sense Of Making Parole In New York, Alexandra Mcglinchy

Dissertations, Theses, and Capstone Projects

For many individuals incarcerated in New York, the initial step toward freedom begins with an interview with the Board of Parole. This process, however, is frequently a complex and challenging one, characterized by repeated denials and extended incarcerations. The disparity in outcomes – where one individual may receive over 20 denials and another is granted parole on their first attempt – highlights the ambiguity and inconsistency in the parole decision-making process. This project aims to clarify the factors that influence parole decisions by concentrating on measurable variables. These include age, race, duration of sentence served, proportion of sentence served, type …

Sparse Bayesian Variable Selection In High‐Dimensional Logistic Regression Models With Correlated Priors, Zhuanzhuan Ma, Zifei Han, Souparno Ghosh, Liucang Wu, Min Wang

School of Mathematical & Statistical Sciences Faculty Publications

In this paper, we propose a sparse Bayesian procedure with global and local(GL) shrinkage priors for the problems of variable selection and classification in high-dimensional logistic regression models. In particular, we consider two types of GL shrinkage priors for the regression coefficients, the horseshoe (HS)prior and the normal-gamma (NG) prior, and then specify a correlated prior for the binary vector to distinguish models with the same size. The GL priors are then combined with mixture representations of logistic distribution to construct a hierarchical Bayes model that allows efficient implementation of a Markov chain Monte Carlo (MCMC) to generate samples from …

Model Selection Through Cross-Validation For Supervised Learning Tasks With Manifold Data, Derek Brown

The Journal of Purdue Undergraduate Research

No abstract provided.

Sensitivity Analysis Of Prior Distributions In Regression Model Estimation, Ayoade I Adewole, Oluwatoyin K. Bodunwa

Al-Bahir

Bayesian inferences depend solely on specification and accuracy of likelihoods and prior distributions of the observed data. The research delved into Bayesian estimation method of regression models to reduce the impact of some of the problems, posed by convectional method of estimating regression models, such as handling complex models, availability of small sample sizes and inclusion of background information in the estimation procedure. Posterior distributions are based on prior distributions and the data accuracy, which is the fundamental principles of Bayesian statistics to produce accurate final model estimates. Sensitivity analysis is an essential part of mathematical model validation in obtaining …

Time Scale Theory On Stability Of Explicit And Implicit Discrete Epidemic Models: Applications To Swine Flu Outbreak, Gülşah Yeni, Elvan Akın, Naveen K. Vaidya

Mathematics and Statistics Faculty Research & Creative Works

Time scales theory has been in use since the 1980s with many applications. Only very recently, it has been used to describe within-host and between-hosts dynamics of infectious diseases. In this study, we present explicit and implicit discrete epidemic models motivated by the time scales modeling approach. We use these models to formulate the basic reproduction number, which determines whether an outbreak occurs, or the disease dies out. We discuss the stability of the disease-free and endemic equilibrium points using the linearization method and Lyapunov function. Furthermore, we apply our models to swine flu outbreak data to demonstrate that the …

On A Multivalued Prescribed Mean Curvature Problem And Inclusions Defined On Dual Spaces, Vy Khoi Le

Mathematics and Statistics Faculty Research & Creative Works

This article addresses two main objectives. First, it establishes a functional analytic framework and presents existence results for a quasilinear inclusion describing a prescribed mean curvature problem with homogeneous Dirichlet boundary conditions, involving a multivalued lower order term. The formulation of the problem is done in the space of functions with bounded variation. The second objective is to introduce a general existence theory for inclusions defined on nonreflexive Banach spaces, which is specifically applicable to the aforementioned prescribed mean curvature problem. This problem can be formulated as a multivalued variational inequality in the space of functions with bounded variation, which, …

Lipschitz Stability For Impulsive Riemann–Liouville Fractional Differential Equations, Martin Bohner, Snezhana Hristova

Mathematics and Statistics Faculty Research & Creative Works

Initial and impulsive conditions for initial value problems of systems of nonlinear impulsive Riemann–Liouville fractional differential equations are introduced. The case when the lower limit of the fractional derivative is changed at each time point of the impulses is studied. In the case studied, the solution has a singularity at the initial time and at any point of the impulses. This leads to the need to appropriately generalize the classical concept of Lipschitz stability. Two derivative types of Lyapunov functions are utilized in order to deduce sufficient conditions for the new stability concept. Three examples are provided for illustration purpose …

Existence Of Solutions By Coincidence Degree Theory For Hadamard Fractional Differential Equations At Resonance, Martin Bohner, Alexander Domoshnitsky, Seshadev Padhi, Satyam Narayan Srivastava

Mathematics and Statistics Faculty Research & Creative Works

Using the Coincidence Degree Theory of Mawhin and Constructing Appropriate Operators, We Investigate the Existence of Solutions to Hadamard Fractional Differential Equations (FRDEs) at Resonance

On A Fully Coupled Nonlocal Multipoint Boundary Value Problem For A Dual Hybrid System Of Nonlinear Q -Fractional Differential Equations, Ahmed Alsaedi, Martin Bohner, Bashir Ahmad, Boshra Alharbi

Mathematics and Statistics Faculty Research & Creative Works

A new class of nonlocal multipoint boundary value problems involving a dual hybrid system of nonlinear Riemann-Liouville-type q-fractional differential equations is studied in this paper. Existence and uniqueness results for the given problem are derived by applying the Leray-Schauder nonlinear alternative and the Banach contraction mapping principle. Examples are presented for illustrating the obtained results. The work established in this paper is a useful contribution to the existing literature on q-fractional differential equations. Some interesting special cases are also discussed.

Critical Point Approaches To Nonlinear Square Root Laplacian Equations, Martin Bohner, Giuseppe Caristi, Shapour Heidarkhani, Amjad Salari

Mathematics and Statistics Faculty Research & Creative Works

This work is devoted to the study of multiplicity results of solutions for a class of nonlinear equations involving the square root of the Laplacian. Indeed, we will use variational methods for smooth functionals, defined on reflexive Banach spaces, in order to achieve the existence of at least three solutions for the equations. Moreover, assuming that the nonlinear terms are nonnegative, we will prove that the solutions are nonnegative. Finally, by presenting an example, we will ensure the applicability of our results.

Open Diameter Maps On Suspensions, Hussam Abobaker, Włodzimierz J. Charatonik, Robert Paul Roe

Mathematics and Statistics Faculty Research & Creative Works

It is shown that if X is a metric continuum, which admits an open diameter map, then the suspension of X, admits an open diameter map. As a corollary, we have that all spheres admit open diameter maps.

Dice Are Blessed Or Cursed, Warren Campbell, Cameron Miller

SEAS Faculty Publications

Dice are cursed or blessed; that is, they roll low or high, but they are never fair. They cannot be manufactured with uniform density and geometric precision. This is particularly true of 20-sided dice or D20s. Faces are smaller than 6-sided dice, and manufacturing tolerances are similar. However, some dice are fairer than others. In our studies of plastic-mold dice about 1 in 4 test fair in 3000 rolls. We have used different statistical tests, including chi-square, modified Kolmogorov Smirnov, and double binomial tests. Of these, the method that consistently performed better is the chi-square goodness of fit test. The …

An Analysis Of Corporate Social Responsibility And Real Earnings Management, Rachel Brassine

Theses, Dissertations and Capstones

Real earnings management (REM) is costly in the form of intense loan restrictions, increased interest expense, and public scrutiny. Nevertheless, companies still practice REM. Based on agency and stakeholder theories, this research predicts that as a company’s CSR score increases, REM will decrease, and this association will become more negative when a critical mass of females on the board of directors exists and when a board-level CSR committee is present. This study also predicts that when a company offers an executive incentive plan based on CSR metrics, REM will decrease, and the relationship will become more negative with a critical …

On The Transmuted Distributions; Properties And Application, Jacob D. Kretzer

Theses, Dissertations and Capstones

The transmuted distributions first appeared in (2007) after Shaw and Buckley constructed a quadratic rank transmutation map (QRTM), G(u) = (1 + λ)u − λu², as a transformation of a cumulative distribution function of a random variable X, to generate the transmuted-X distribution. In (2017), Jayakumar & Babu defined the T -transmuted-X family of distributions by incorporating a transmuted-X into a transformer-transformed class of distributions (Aljarrah et al., 2014). This thesis surveys the main properties of the transmuted-X distribution, such as density shapes, moments, and entropy. Detailed attention will be given …

Gender Wage Gap: Analysis Of Women In Statistical Industries And Financial Effects Of The Wage Gap, Renee Delos

Honors Projects in Mathematics and Economics

Women in statistical and mathematical industries are continuing to be mistreated within the workforce. Women are constantly being paid less than men, despite prior qualifications that should show otherwise. An overall pay gap between male and female employees not only affects the women’s work at the company, but also their lives outside of the corporate world. This thesis analyzes the direct effects of this pay difference, and how women are truly treated throughout the working world. The data collected through surveys is used as the basis in determining the real pay gap, and the overall emotions and degree of comfort …

Ms Environmental Biology Capstone Project, Denise Corona

Regis University Student Publications (comprehensive collection)

Land-use change (LUC) is a key driver of biodiversity loss, altering the structure and function of ecosystems through human activities such as urbanization and agriculture. This change has led to habitat loss and fragmentation, resulting in the rapid decline of avian populations globally. Wildlife rehabilitation centers are the primary responders for injured birds and their records provide valuable data to monitor potential factors impacting bird populations. However, these datasets are underutilized in research. This study examined how LUC in the Front Range affects the likelihood and circumstances of admission of injured birds to the Rocky Mountain Wildlife Alliance (RMWA) in …

Impact Of Non-Medical Cannabis Legalization With Market Restrictions On Health Service Use And Incident Cases Of Psychotic Disorder In Ontario, Canada, Kelly K. Anderson, Rebecca Rodrigues, Britney Le, Maliha Mamun, Suzanne Archie, Jordan Edwards, Tara Elton-Marshall, Jason Gilliland, Daniel Thomas Myran, Lena Palaniyappan, Christopher M Perlman, Jamie A Seabrook, Robin M Murray, Salimah Z Shariff

Epidemiology and Biostatistics Publications

BACKGROUND: Cannabis is a risk factor in the onset and persistence of psychotic disorders. There is concern that non-medical cannabis legalization in Canada may have population-level impacts on psychotic disorders. We sought to examine changes in health service use and incident cases of psychotic disorder following cannabis legalization, during a period of tight restrictions on retail stores and product types.

METHODS: We conducted a cross-sectional interrupted time-series analysis using linked population-based health administrative data from Ontario (Canada) from January 2014 to March 2020. We identified psychosis-related outpatient visits, emergency department visits, hospitalizations, and inpatient length of stay, as well as …

Perioperative Mortality In Low-, Middle-, And High-Income Countries: Protocol For A Multi-Level Meta-Regression Analysis, Kevin Mcintyre, Yun-Hee Choi, Ava John-Baptiste, Daniel J Lizotte, Eunice Y S Chan, Jessica Moodie, Saverio Stranges, Janet Martin

Epidemiology and Biostatistics Publications

BACKGROUND: Surgery is an indispensable component of a functional healthcare system. To date there is limited information regarding how many people die during the perioperative period globally. This study describes a protocol for a systematic review and multilevel meta-regression to evaluate time trends regarding the odds of perioperative mortality among adults undergoing a bellwether surgical procedure while accounting for higher order clustering at the national level.

METHODS: Published studies reporting the number of perioperative deaths from bellwether surgical procedures among adults will be identified from MEDLINE, Embase, Cochrane CENTRAL, LILACS and Global Index Medicus. The primary outcome will be the …

Using Ai For Qualitative Labeling: Consistency And Comparisons, James Mcintyre

Honors Program Theses

This paper details a research study evaluating AI's ability to perform qualitative deductive coding. Multiple AI models were utilized and compared against three human coders and one expert coder. A series of 107 statements were sourced from a group discussion for a qualitative impact assessment of an organization. The AI models were provided these statements and directed to code them using the Community Capitals Framework. Two generations of AI models were evaluated. Overall, the AI achieved a fair level of agreement with the human annotators, but the alignment was far from perfect. Newer AI models did not increase agreement with …

Es/Ph/Sra 511: Applied Statistics, Minsoo Kang

GMAS Course Syllabi

No abstract provided.

Teacher Resilience: Validating An Instrument In The Us Context, Joy Mccreary

CGU Theses & Dissertations

The research surrounding teacher resilience is an emerging field, and the cultivation of instruments that measure teacher resilience should be bolstered. Specifically, there is a need for the creation of an instrument to be used in the United States. To address this need, I piloted the Multidimensional Teachers’ Resilience Scale (Mansfield & Wosnitza, 2015; Peixoto et al., 2020) in the US. The Multidimensional Teachers’ Resilience Scale (MTRS) comprises 29 items on four dimensions of resilience (professional, motivational, social, and emotional). The MTRS shows decent reliability and validity, but also demonstrates a need for a stronger instrument to be developed. As …

Evaluation Of Imputation Methods Focusing On Categorical Outcomes, Nadia Bernardo Mendoza

CGU Theses & Dissertations

In general, standard statistical analysis models typically rely on completely observed cases, excluding incomplete rows from the dataset. This approach poses particular challenges when the objective is to predict a rare outcome, especially when some of the ob servations with the rare outcome are incomplete. In such cases, the available information to support the model in predicting this event is reduces. Theoretically correct models may pre dict all instances in the majority class achieving high accuracy, but fail in predicting the rare cases, which are often the most interesting ones. Therefore, it is crucial to make the most of all …

A Computerized Mastitis Classification Aid Using A Dairy Herd-Based Records: Multi-Layer Perceptron (Mlp) Neural Network With Backpropagation Approach, Ahmed M. Gad Prof, Dina Faris De, Sherif Ramadan Prof, Ghada Afifi Dr, Eman Manaa Prof, Mahmoud El-Tarabany Prof

Business Administration

The main objective of this study is to develop an efficient machine learning-based model for the early prediction of clinical mastitis in Holstein Friesian dairy cattle where automatic milking system (AMS) data is used. The model aims to offer a costless opportunity for mastitis control and reduce its negative impact on livestock production. Different forward multilayer perceptron (MLP) neural networks with backpropagation (BP) learning algorithms using various numbers of hidden neurons and epochs have been introduced. The results of the established models are evaluated based on different metrics such as the accuracy, the F1 core, the precision, the recall, and …

Understanding Social Dynamics In Toxic Conversations And Public Health Intervention Acceptance On Social Media, Ana Aleksandric

Computer Science and Engineering Dissertations - Archive

Social media is now central to daily life, offering users a space to share content and opinions. However, these platforms also facilitate the spread of hate speech and misinformation, which can negatively impact public health. This dissertation develops methodologies to analyze social media data for insights that could inform health interventions. The research first examines user responses to toxic content, focusing on behavioral and emotional reactions, as well as group dynamics and bystander effects in toxic interactions. Another key focus is public opinion toward health interventions, particularly COVID-19 vaccination, using geolocated posts and analyzing factors such as race, ethnicity, and …

Advancing Deep Learning With Graph-Based Structural Insights: From Graph Classification To Semantic Segmentation, Xin Ma

Computer Science and Engineering Dissertations - Archive

Deep learning has profoundly transformed machine learning by offering sophisticated data representations, yet effectively incorporating structural information remains a challenge. Structural data, whether explicit or implicit, has the potential to significantly enhance the performance of deep learning tasks. This research investigates the benefits of structural information across three crucial tasks: classification, clustering, and segmentation. For explicit structural data, where inputs are directly represented as graphs, we investigate graph-level classification in brain connectivity networks. We introduce the Multi-resolution Edge Network (MENET), a novel framework designed to identify disease-specific connectomic benchmarks with high discriminatory power across diagnostic categories. MENET leverages graph-level representations …