Open Access. Powered by Scholars. Published by Universities.®
Other Statistics and Probability Commons™
Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Applied Statistics (7)
- Social and Behavioral Sciences (7)
- Statistical Models (6)
- Computer Sciences (4)
- Education (4)
-
- Sociology (4)
- Statistical Methodology (4)
- Artificial Intelligence and Robotics (3)
- Mathematics (3)
- Quantitative, Qualitative, Comparative, and Historical Methodologies (3)
- Statistical Theory (3)
- Applied Mathematics (2)
- Business (2)
- Data Science (2)
- Educational Assessment, Evaluation, and Research (2)
- Longitudinal Data Analysis and Time Series (2)
- Other Computer Sciences (2)
- Probability (2)
- Algebra (1)
- Analysis (1)
- Biostatistics (1)
- Child Psychology (1)
- Community-Based Research (1)
- Computer Engineering (1)
- Curriculum and Instruction (1)
- Developmental Psychology (1)
- Educational Psychology (1)
- Institution
- Keyword
-
- Morgridge College of Education (9)
- Research Methods and Information Science (9)
- Research Methods and Statistics (9)
- Grounded theory (2)
- Machine learning (2)
-
- ANOVA (1)
- Adolescent (1)
- Adverse childhood experiences (1)
- Bayesian (1)
- Beta Distribution (1)
- Bias (1)
- Bootstrap (1)
- Classification (1)
- Collective action network (1)
- Community-based resource management (1)
- Concept drift (1)
- Confidence Interval (1)
- Corequisite math (1)
- Crossing times (1)
- Crossing times models (1)
- Curse of dimensionality (1)
- D.A.R.E. (1)
- Data-driven deep learning (1)
- Developmental math (1)
- Distribution theory (1)
- Dynamic measurement modeling (1)
- Education (1)
- Educational attainment predictions (1)
- Energy consumption prediction (1)
- Energy efficiency (1)
Articles 1 - 22 of 22
Full-Text Articles in Other Statistics and Probability
The Use Of Regularization To Detect Racial Inequities In Pay Equity Studies: An Empirical Study And Reflections On Regulation Methods, Christopher M. Peña
The Use Of Regularization To Detect Racial Inequities In Pay Equity Studies: An Empirical Study And Reflections On Regulation Methods, Christopher M. Peña
Electronic Theses and Dissertations
Since the late 1970s, multiple linear regression has been the preferred method for identifying discrimination in pay. An empirical study on this topic was conducted using quantitative critical methods. A literature review first examined conflicting views on using multiple linear regression in pay equity studies. The review found that multiple linear regression is used so prevalently in pay equity studies because the courts and practitioners have widely accepted it and because of its simplicity and ability to parse multiple sources of variance simultaneously. Commentaries in the literature cautioned about errors in model specification, the use of tainted variables, and the …
A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman
A Data-Driven Multi-Regime Approach For Predicting Real-Time Energy Consumption Of Industrial Machines., Abdulgani Kahraman
Electronic Theses and Dissertations
This thesis focuses on methods for improving energy consumption prediction performance in complex industrial machines. Working with real-world industrial machines brings several challenges, including data access, algorithmic bias, data privacy, and the interpretation of machine learning algorithms. To effectively manage energy consumption in the industrial sector, it is essential to develop a framework that enhances prediction performance, reduces energy costs, and mitigates air pollution in heavy industrial machine operations. This study aims to assist managers in making informed decisions and driving the transition towards green manufacturing. The energy consumption of industrial machinery is substantial, and the recent increase in CO2 …
Penalized Bayesian Exponential Random Graph Models., Vicki Modisette
Penalized Bayesian Exponential Random Graph Models., Vicki Modisette
Electronic Theses and Dissertations
Networks have the critical ability to represent the complex interconnectedness of social relationships, biological processes, and the spread of diseases and information. Exponential random graph models (ERGM) are one of the popular statistical methods for analyzing network data. ERGM, however, struggle with computational challenges and degeneracy issues, further exacerbated by their inability to handle high-dimensional network data. Bayesian techniques provide a promising avenue to overcome these two problems. This paper considers penalized Bayesian exponential random graph models with adaptive lasso and adaptive ridge penalties to perform variable selection and reduce multicollinearity on a variety of networks. The experimental results demonstrate …
Network Intrusion Detection Using Deep Reinforcement Learning, Hamed T. Sanusi
Network Intrusion Detection Using Deep Reinforcement Learning, Hamed T. Sanusi
Electronic Theses and Dissertations
This thesis delves into cybersecurity by applying Deep Reinforcement(DRL) Learning in network intrusion detection. One advantage of DRL is the ability to adapt to changing network conditions and evolving attack methods, making it a promising solution for addressing the challenges involved in intrusion detection. The thesis will also discuss the obstacles and benefits of using Classification methods for network intrusion detection and the need for high-quality training data. To train and test our proposed method, the NSL-KDD dataset was used and then adjusted by converting it from a multi-classification to a binary classification, achieved by joining all attacks into one. …
Examining The Credibility Of Story-Based Causal Methodologies, Megan E. Kauffmann
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 …
Expanding The Network Evaluation Toolkit: Combining Social Network Analysis & Qualitative Comparative Analysis, Debbie Gowensmith
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 …
Confidence Interval For The Mean Of A Beta Distribution, Sean Rangel
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.
Prediction Intervals: The Effects And Identification Of Sparse Regions For Nonparametric Regression Methods, Jackson Faires
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 …
Moving Past ‘One Size Fits All’: Developing A Trajectory Deviance Index For Dynamic Measurement Modeling, Yixiao Dong
Moving Past ‘One Size Fits All’: Developing A Trajectory Deviance Index For Dynamic Measurement Modeling, Yixiao Dong
Electronic Theses and Dissertations
Dynamic Measurement Modeling (DMM) is a recently developed measurement framework for gauging developing constructs (e.g., learning capacity) that conventional single-timepoint tests cannot assess. Like most measurement models, overall model fit indices of DMM do not indicate the measurement appropriateness for each included student. For this reason, other measurement modeling paradigms (e.g., Item-Response Theory; IRT) utilize person-fit or model appropriateness statistics to indicate whether a measurement model appropriately describes the data from each individual student. However, within the extant DMM framework, no statistical index has yet been developed for this purpose. Thus, the current project advanced a person-specific DMM Trajectory Deviance …
Statistical Modeling Of Positive Peer Support On Longitudinal Adolescent Substance Use, Kady Rost
Statistical Modeling Of Positive Peer Support On Longitudinal Adolescent Substance Use, Kady Rost
Electronic Theses and Dissertations
To evaluate this study’s research question of ”Does the latent construct of Positive Peer Support (PPS) relate to the construct of Adolescent Substance Use (ASU) over time, controlling for neighborhood safety, race, and sex?”, Structural Equation (SEM) and Latent Growth Curve Modeling (LGCM) were used to investigate trajectories. Secondary longitudinal data from Zimmerman (2014) of 604 students enrolled for four consecutive years in public schools located in Flint, Michigan. In the secondary data resource, students who participated were declared “at risk” by GPA. Significant relationships were found in SEM: Positive Peer Support to Adolescent Substance Use, All Control Variables to …
Assessing The Variations Of Educational Attainment At National And Subnational Levels Using Hierarchical Linear Models, Bingxin Qi
Electronic Theses and Dissertations
Education is a human right, and equal access to education is not only crucial for an individual’s well-being, but also essential for eradicating poverty, ensuring long-term prosperity for all, transforming the society, and achieving sustainable development. Measuring education development, especially the variations of educational attainment, in a timely and accurate manner can help educators, practitioners, scientists, and policymakers compare and evaluate various education indicators at both subnational and national levels. This research presents an approach that combines multi-source and multidimensional data including population distribution, human settlement, and education data to assess and explore educational attainment trajectories at both national and …
A Grounded Theory Inquiry Into The Pedagogical Socialization Of Graduate Students Within Graduate Quantitative Methods Courses, Amanda Kay Thomas
A Grounded Theory Inquiry Into The Pedagogical Socialization Of Graduate Students Within Graduate Quantitative Methods Courses, Amanda Kay Thomas
Electronic Theses and Dissertations
Quantitative methods are one of the most highly technical fields of study within social sciences graduate programs. Although classroom pedagogy is an important factor connected to student success within graduate quantitative methods courses little is known on the pedagogical socialization experiences of masters and doctoral students. The purpose of this grounded theory inquiry was to discover graduate students perspectives on their pedagogical socialization experiences and the norms, values and role expectations transmitted during the teaching and learning of quantitative methods. Narrative data was collected from in-depth interviews among a theoretical sample of 31 masters and doctoral students enrolled in introductory, …
Use Of Research Tradition And Design In Program Evaluation: An Explanatory Mixed Methods Study Of Practitioners’ Methodological Choices, Margaret Schultz Patel
Use Of Research Tradition And Design In Program Evaluation: An Explanatory Mixed Methods Study Of Practitioners’ Methodological Choices, Margaret Schultz Patel
Electronic Theses and Dissertations
The goal of this explanatory sequential mixed method study was to assess whether there were observable trends, associations, or group differences in evaluation methodology by settings and content area in published evaluations from the past ten years (quantitative), to illuminate how evaluation practitioners selected these methodologies (qualitative), and assess how emergent findings from each phase fit together or helped contextualize each other. In this study, methodology was operationalized as research tradition and method was operationalized as research design. For phase one (quantitative), a systematized ten-year review of five peer-reviewed evaluation journals was conducted and coded by journal, research tradition, research …
The Effects Of Adverse Childhood Experiences On Behavioral Outcomes, Jennifer Thomas
The Effects Of Adverse Childhood Experiences On Behavioral Outcomes, Jennifer Thomas
Electronic Theses and Dissertations
This study intends to explore the intersection of two vulnerable populations, early childhood development and risks associated with exposure to adverse childhood experiences (ACEs). This study examines how age plays a role in the long-term relationship between ACEs and internal and external behaviors. This study seeks to answer the question of: How does age influence the relationship between number of ACEs and internal and external behaviors? The participants in this study include those aged 0 – 16 from the National Survey of Child and adolescent Well-Being (NSCAW) dataset. The NSCAW study consists of five waves of data where Wave I …
Is Corequisite Developmental Math Effective At East Tennessee State University?, Christine Padden
Is Corequisite Developmental Math Effective At East Tennessee State University?, Christine Padden
Electronic Theses and Dissertations
This thesis looks at the corequisite developmental math program at East Tennessee State University (ETSU) and compares the effectiveness to the previous developmental math program by comparing the student outcomes in MATH 1530. MATH 1530 is a non-calculus based statistic and probability course that satisfies most majors’ general education math requirements. ETSU sees approximately 1,000 students a year pass through MATH 1530 which is around 6.7% of the total enrollment at ETSU[9]. We are interested in the last five years of the developmental math program before it was changed to corequisite developmental math and the first five years of corequisite …
Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr
Regression Tree Construction For Reinforcement Learning Problems With A General Action Space, Anthony S. Bush Jr
Electronic Theses and Dissertations
Part of the implementation of Reinforcement Learning is constructing a regression of values against states and actions and using that regression model to optimize over actions for a given state. One such common regression technique is that of a decision tree; or in the case of continuous input, a regression tree. In such a case, we fix the states and optimize over actions; however, standard regression trees do not easily optimize over a subset of the input variables\cite{Card1993}. The technique we propose in this thesis is a hybrid of regression trees and kernel regression. First, a regression tree splits over …
Evaluation Of Using The Bootstrap Procedure To Estimate The Population Variance, Nghia Trong Nguyen
Evaluation Of Using The Bootstrap Procedure To Estimate The Population Variance, Nghia Trong Nguyen
Electronic Theses and Dissertations
The bootstrap procedure is widely used in nonparametric statistics to generate an empirical sampling distribution from a given sample data set for a statistic of interest. Generally, the results are good for location parameters such as population mean, median, and even for estimating a population correlation. However, the results for a population variance, which is a spread parameter, are not as good due to the resampling nature of the bootstrap method. Bootstrap samples are constructed using sampling with replacement; consequently, groups of observations with zero variance manifest in these samples. As a result, a bootstrap variance estimator will carry a …
Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don
Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don
Electronic Theses and Dissertations
In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …
Old English Character Recognition Using Neural Networks, Sattajit Sutradhar
Old English Character Recognition Using Neural Networks, Sattajit Sutradhar
Electronic Theses and Dissertations
Character recognition has been capturing the interest of researchers since the beginning of the twentieth century. While the Optical Character Recognition for printed material is very robust and widespread nowadays, the recognition of handwritten materials lags behind. In our digital era more and more historical, handwritten documents are digitized and made available to the general public. However, these digital copies of handwritten materials lack the automatic content recognition feature of their printed materials counterparts. We are proposing a practical, accurate, and computationally efficient method for Old English character recognition from manuscript images. Our method relies on a modern machine learning …
Some New And Generalized Distributions Via Exponentiation, Gamma And Marshall-Olkin Generators With Applications, Hameed Abiodun Jimoh
Some New And Generalized Distributions Via Exponentiation, Gamma And Marshall-Olkin Generators With Applications, Hameed Abiodun Jimoh
Electronic Theses and Dissertations
Three new generalized distributions developed via completing risk, gamma generator, Marshall-Olkin generator and exponentiation techniques are proposed and studied. Structural properties including quantile functions, hazard rate functions, moment, conditional moments, mean deviations, R\'enyi entropy, distribution of order statistics and maximum likelihood estimates are presented. Monte Carlo simulation is employed to examine the performance of the proposed distributions. Applications of the generalized distributions to real lifetime data are presented to illustrate the usefulness of the models.
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku
Takens Theorem With Singular Spectrum Analysis Applied To Noisy Time Series, Thomas K. Torku
Electronic Theses and Dissertations
The evolution of big data has led to financial time series becoming increasingly complex, noisy, non-stationary and nonlinear. Takens theorem can be used to analyze and forecast nonlinear time series, but even small amounts of noise can hopelessly corrupt a Takens approach. In contrast, Singular Spectrum Analysis is an excellent tool for both forecasting and noise reduction. Fortunately, it is possible to combine the Takens approach with Singular Spectrum analysis (SSA), and in fact, estimation of key parameters in Takens theorem is performed with Singular Spectrum Analysis. In this thesis, we combine the denoising abilities of SSA with the Takens …
Level Crossing Times In Mathematical Finance, Ofosuhene Osei
Level Crossing Times In Mathematical Finance, Ofosuhene Osei
Electronic Theses and Dissertations
Level crossing times and their applications in finance are of importance, given certain threshold levels that represent the "desirable" or "sell" values of a stock. In this thesis, we make use of Wald's lemmas and various deep results from renewal theory, in the context of finance, in modelling the growth of a portfolio of stocks. Several models are employed .