Applied Statistics Commons^™

Machine Learning Approaches For Cyberbullying Detection, Roland Fiagbe

Data Science and Data Mining

Cyberbullying refers to the act of bullying using electronic means and the internet. In recent years, this act has been identifed to be a major problem among young people and even adults. It can negatively impact one’s emotions and lead to adverse outcomes like depression, anxiety, harassment, and suicide, among others. This has led to the need to employ machine learning techniques to automatically detect cyberbullying and prevent them on various social media platforms. In this study, we want to analyze the combination of some Natural Language Processing (NLP) algorithms (such as Bag-of-Words and TFIDF) with some popular machine learning …

Variable Selection For High-Dimensional Data With Interaction Effects: Methods, Applications, And Inferences, Leiyue Li

Theses and Dissertations--Statistics

For high-dimensional data where the number of variables greatly exceeds the number of observations, selecting important variables while maintaining the required heredity conditions can be challenging. This dissertation is structured into three interconnected parts. In the first part, we propose a variable selection method by implementing a well-known optimization technique, the Genetic Algorithm. An R package was developed to simplify the implementation and usage of the proposed method. We then propose another variable selection method by extending the study from the Genetic Algorithm to a different but related optimization technique, Simulated Annealing. We consider three different hierarchical structures in both …

From Non-Parametric Methods To Self-Supervised Learning: Applications In Edge Detection And Image Denoising, Jiacheng Xu

Theses and Dissertations--Statistics

This dissertation explores advanced methodologies for edge detection and image denoising through the application of both traditional non-parametric methods and modern self-supervised deep learning techniques. Beginning with non-parametric approaches, we refine surface fitting and jump detection criteria to enhance the detection of discontinuous regression surfaces in grayscale images. These foundational techniques are extended to color images, with analyses across RGB and CIELAB color spaces to improve edge detection accuracy. We then introduce a self-supervised neural network model that integrates Masked Modeling into the Bi-Directional Cascade Network (BDCN) framework. This approach shows the potential of reducing the dependency on annotated data …

Bar-Code Variable: A Novel Approach To Efficiently Find Interaction Effects, Lee Sak Park

Theses and Dissertations--Statistics

This paper introduces the bar-code variable, a novel method for processing a sequence of binary explanatory variables efficiently in the linear regression modeling framework. Represented as an integer or a sequence of bits, the bar-code variable captures infor- mation on original binary variables and their potential interaction effects. Utilizing the bar-code variable, the study explores streamlined feature selection in linear re- gression modeling with binary explanatory variables. The paper demonstrates how the bar-code variable, through re-parameterization, facilitates the transition from cell means estimates, µ̂, in the cell-means ANOVA model to coefficient estimates, β̂, in the linear regression model, and vice …

Computation Of Separate Ratio And Regression Estimator Under Neutrosophic Stratified Sampling: An Application To Climate Data, Abhishek Singh, Hemant Kulkarni, Florentin Smarandache, Gajendra K. Vishwakarma

Branch Mathematics and Statistics Faculty and Staff Publications

In this article, we introduce a novel approach by presenting separate ratio and regression estimators in the context of neutrosophic stratified sampling for the very first time, incorporating auxiliary variables. We have conducted a thorough analysis to estimate these newly proposed estimators' bias and mean square error (MSE) up to the first-order approximation. Theoretically using efficiency comparison criteria, our findings demonstrate the superior performance of these estimators compared to traditional unbiased estimators. Also, numerically based on real-life and artificial data, we have shown the supremacy of the neutrosophic stratified sampling over neutrosophic simple random sampling along with the supremacy of …

Numerical Investigation And Statistical Analysis Of The Flow Patterns Behind Square Cylinders Arranged In A Staggered Configuration Utilizing The Lattice Boltzmann Method, M. Abid, N. Yasin, M. Saqlain, S. Ul-Islam, S. Ahmad

Mathematics & Statistics Faculty Publications

Flow past bluff bodies like square cylinders is important in engineering applications, but flow patterns behind staggered cylinder arrangements remain poorly understood. Existing studies have focused on tandem or side-by-side configurations, while offset orientations have received less attention. The aim of this paper is to numerically investigate flow dynamics and force characteristics behind two offset square cylinders using the single relaxation time lattice Boltzmann method. The effects of changing both the Reynolds number (Re = 1-150) and gap spacing ratio (g* = 0.5-5) between the cylinders are analyzed. Instantaneous vorticity contours, time histories of drag and lift coefficients, power spectra …

Pitching The Use Of Squared And Interaction Terms In Regression Via Baseball Heat Maps, Lucas Chepelsky

Williams Honors College, Honors Research Projects

This project will examine the impact of using second-order terms in regression. For illustration, we use an example of regression where a baseball player's three by three heat map, including the height and distance from inside to outside of the pitch, are variables used to predict batting average. We find that second-order terms are crucial in discovering nonlinear relationships and interaction effects in regression models, and maintain that the common practice of using first-order additive models is insufficient.

To Mean Or Not To Mean: An Investigation Of Regression To The Mean, Hunter Ellis

Williams Honors College, Honors Research Projects

Regression to the mean is a statistical phenomenon that can hide important characteristics of what is truly happening in a research study. Caused by statistical randomness, regression to the mean occurs when extreme values, high or low, are followed by less extreme values. To correctly deal with it, one must understand what it is and how to distinguish its effect on conclusions made from the data. This paper provides examples of regression to the mean in both a medical and academic performance study and explains simple identifiers one can observe. Those are then followed up by the introduction of the …

Ensemble Classification: An Analysis Of The Random Forest Model, Jarod Korn

Williams Honors College, Honors Research Projects

The random forest model proposed by Dr. Leo Breiman in 2001 is an ensemble machine learning method for classification prediction and regression. In the following paper, we will conduct an analysis on the random forest model with a focus on how the model works, how it is applied in software, and how it performs on a set of data. To fully understand the model, we will introduce the concept of decision trees, give a summary of the CART model, explain in detail how the random forest model operates, discuss how the model is implemented in software, demonstrate the model by …

Statistical Modeling Of Bankruptcy Data, Andrew Elsfelder

Williams Honors College, Honors Research Projects

My project uses a dataset of bankrupt and non-bankrupt companies in Taiwan from 1999 to 2009. This data was collected from the Taiwan Economic Journal. The statistical methods I used to model the data are CHAID, CART, and logistic regression. The models created are tools that can predict if a company is bankrupt, or not-bankrupt based on other data about the company. I created multiple models for each of the methods to find the best model for each method. I then analyzed the output from each method. Lastly, I determined which model was the best for this data based on …

A Comparative Analysis Of A Family Of Advanced Iterative Optimization Methods In Nonlinear Regression, Tanmoy Kumar Debnath

College of Graduate Studies: Theses & Dissertations

Classical statistical supervised learning optimization techniques like the Gauss-Newton Iterative Method (GNIM), Weighted Gauss-Newton Iterative Method (WGNIM), Reweighted Gauss-Newton Iterative Method (RGNIM), and Levenberg-Marquart (LM) algorithm extend the nonlinear least squares method. The WGNIM improves model fitting by controlling heteroscedasticity in the linear and nonlinear models. A comparative analysis of the GNIM, WGNIM, RGNIM, and LM methods for fitting nonlinear models is presented. A step-wise diagnosis for structural multicollinearity in the reweighted linearized model is investigated via the Variance Inflation Factor (VIF) to determine variance inflation in the sequence of estimators for the model parameters. Under restricted multicollinearity levels in …

Accounting For Variability Due To Resampling Using Bootstrapping, Dipendra Phuyal

College of Graduate Studies: Theses & Dissertations

Bradley Efron (1979) introduced bootrapping. Typically a researcher is interested in studying a process which generates individuals. The collection of individuals the process has(actual) or could have (conceptual) generated is the population. The collection of conceptual members of the population is an uncountable collection. Hence, the population is anuncountable collection of individuals. The collection of individuals the process has generated (actual individuals) is representative of what the process can generate and will bereferred to as the representative sample. The size of this sample is a nonnegative integervalued random variable N which may be a constant random variable such as in …

Investigating Flash Flood Occurrence Using Negative Binomial Models In Maryland, United States Of America, Zainab O. Akinsemoyin

College of Graduate Studies: Theses & Dissertations

Globally, as extreme weather patterns intensify, flash floods have emerged as one of the most destructive and immediate environmental threats. In Maryland, flash floods are particularly concerning due to its diverse topography and increasing urban development, which exacerbates runoff and overwhelms drainage systems. The state has experienced significant flash flood events, highlighting the need for effective models to manage risks and inform mitigation strategies. While regression models such as the Negative Binomial (NB) and Zero-Inflated Negative Binomial (ZINB) are commonly used for count data analysis, their application to flash flood modeling in the USA, including regions like Maryland, remains limited …

Classification In Supervised Statistical Learning With The New Weighted Newton-Raphson Method, Toma Debnath

College of Graduate Studies: Theses & Dissertations

In this thesis, the Weighted Newton-Raphson Method (WNRM), an innovative optimization technique, is introduced in statistical supervised learning for categorization and applied to a diabetes predictive model, to find maximum likelihood estimates. The iterative optimization method solves nonlinear systems of equations with singular Jacobian matrices and is a modification of the ordinary Newton-Raphson algorithm. The quadratic convergence of the WNRM, and high efficiency for optimizing nonlinear likelihood functions, whenever singularity in the Jacobians occur allow for an easy inclusion to classical categorization and generalized linear models such as the Logistic Regression model in supervised learning. The WNRM is thoroughly investigated …

Optimizing Sports Outcome Prediction Through Feature Engineering And Machine Learning, Vitor S. Freitas

Graduate Theses/Dissertations

The challenge of predicting the outcome of a team game lies in the high complexity and dynamics of the sports data. This thesis focuses on the aspect of using feature engineering and the genetic algorithm to predict the winner and the score of various sports events. Generally, it deals with how machine learning algorithms are combined with state-of-the-art feature engineering techniques in sports datasets derived from various sports disciplines. In this thesis, five different machine learning models have been applied, classification and regression trees (CART), random forest (RF), stochastic gradient boosting (SGB), eXtreme gradient boosting (XGBoost), and extreme learning machine …

Title: I: L1-Norm Matrix Completion For Recommender Systems Ii: Conjecturing-Based Classification, Fatemeh Valizadeh Gamchi

Theses and Dissertations

Recommendation systems are essential for providing personalized user experiences, but their performance can be affected by outliers especially in traditional collaborative filtering methods that use the L2-norm. To address this challenge, we developed two new algorithms, SharpEl1rs and SharpEl1rs-Impute, based on the L1-norm to improve resistance against extreme values and effectively handle missing data. Our experimental setting was designed to compare these proposed methods with existing techniques. Then our algorithms are applied to real datasets to assess their performance, with findings indicating that our proposed models offer improved accuracy in some cases and solid performance in others for industrial-scale recommendation …

Influence Of Attack Performance On The Ovc Volleyball Regular Seasons 2022 & 2023, Ignacio Valdemoros

Masters Theses

Understanding the outcome of volleyball games is necessary for coaches before, after, and during a season. There are several ways to gain this understanding, but statistical analysis is fundamental to see the minimum patterns of behavior that influence wins and losses in Volleyball. Furthermore, this analysis helps identify the optimal approach to achieving a goal and determining the most effective alternative to success. Scoring points in Volleyball involves three key skills: serving, blocking, and attacking. Among these skills, attacking plays the most relevant role in determining the outcome of a match. The position on the court (e.g. Outside Hitter, Middle …

Advanced Techniques In Time Series Forecasting: From Deterministic Models To Deep Learning, Xue Bai

Graduate Theses, Dissertations, and Problem Reports (ETD)

This dissertation discusses three instances of temporal prediction, applied to population dynamics and deep learning.

In population modeling, dynamic processes are frequently represented by systems of differential equations, allowing for the analysis of various phenomena. The first application explores modeling cloned hematopoiesis in chronic myeloid leukemia (CML) via a nonlinear system of differential equations. By tracking the evolution of different cell compartments, including cycling and quiescent stem cells, progenitor cells, differentiated cells, and terminally differentiated cells, the model captures the transition from normal hematopoiesis to the chronic and accelerated-acute phases of CML. Three distinct non-zero steady states are identified, representing …

The Distribution Of The Significance Level, Paul O. Monnu

College of Graduate Studies: Theses & Dissertations

Reporting the p-value is customary when conducting a test of hypothesis or significance. The likelihood of getting a fictitious second sample and presuming the null hypothesis is correct is the p-value. The significance level is a statistic that interests us to investigate. Being a statistic, it has a distribution. For the F-test in a one-way ANOVA and the t-tests for population means, we define the significance level, its observed value, and the observed significance level. It is possible to derive the significance level distribution. The t-test and the F-test are not without controversy. Specifically, we demonstrate that as sample size …

Investigation Of The Effect Of Preparation Parameters On The Structural And Mechanical Properties Of Gelatin/Elastin/Sodium Hyaluronate Scaffolds Fabricated By The Combined Foaming And Freeze-Drying Techniques, Mansour Qamash, S. Misagh Imani, Meisam Omidi, Ciara Glancy, Lobat Tayebi

Electrical & Computer Engineering Faculty Publications

This paper aimed to evaluate the effects of different preparation parameters, including agitation speed, agitation time, and chilling temperature, on the structural and mechanical properties of a novel gelatin/elastin/sodium hyaluronate tissue engineering scaffold, recently developed by our research group. Fabricated using a combination of foaming and freeze-drying techniques, the scaffolds were assessed to understand how these parameters influence their morphology, internal microstructure, porosity, mechanical properties, and degradation behavior. The fabrication process used in this study involved preparing a homogeneous aqueous solution containing 8% gelatin, 2% elastin, and 0.5% sodium hyaluronate (w/v), which was then subjected to mechanical agitation at speeds …