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Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter
Fractional Random Weighted Bootstrapping For Classification On Imbalanced Data With Ensemble Decision Tree Methods, Sean Charles Carter
USF Tampa Graduate Theses and Dissertations
Ensemble methods are commonly used for building predictive models for classification. Models that are unstable to perturbations in the training set, such as the decision tree, often see considerable reductions in error when grouped, using bootstrapped resamples of the training data to train many models. The non-parametric bootstrap, however, has limited efficacy when used on severely imbalanced data, especially when the number of observations of one or more classes is exceptionally small. We explore the fractional random weighted bootstrap, which randomly assigns fractional weights to observations, as an alternative resampling pro cedure in training machine learning ensembles, particularly decision tree …
Probabilistic Modeling Of Democracy, Corruption, Hemophilia A And Prediabetes Data, A. K. M. Raquibul Bashar
Probabilistic Modeling Of Democracy, Corruption, Hemophilia A And Prediabetes Data, A. K. M. Raquibul Bashar
USF Tampa Graduate Theses and Dissertations
Parametric analysis of any real-world data is the most powerful tool to characterize the probabilistic behavior in social, economic, medical, epidemiological, and other areas of study. In the present study, we identify the theoretical Probability Distribution Function(PDF) for Democracy Index Scores (DIS) from the Economist Intelligence Unit (EIU) database and estimate the maximum likelihood estimates of the theoretical PDFS. We also identify the individual PDFs for each of the clusters, Full Democracy, Flawed Democracy, Hybrid Regime, and Authoritarian Regime defined by the Economist Intelligence Unit (EIU).
A statistical model is a convenient instrument to predict the future value of any …
Graphicacy For Numeracy: Review Of Fundamentals Of Data Visualization: A Primer On Making Informative And Compelling Figures By Claus O. Wilke (2019), Christy M. Bebeau
Graphicacy For Numeracy: Review Of Fundamentals Of Data Visualization: A Primer On Making Informative And Compelling Figures By Claus O. Wilke (2019), Christy M. Bebeau
Numeracy
Wilke, Claus O. 2019. Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures. (Sebastopol, CA: O’Reilly Media, Inc.). 390 pp. ISBN 978-1-492-03108-6. First edition. First release: 03-15-2019.
Claus O. Wilke has authored an excellent reference about producing and understanding static figures, figures used online, in print, and for presentations. His book is neither a statistics nor programming text, but familiarity with basic statistical concepts is helpful. Written in three parts, the book presents both the math and artistic design aspects of telling a story through figures. Wilke makes extensive use of examples, labels them good, bad, …
Taking Multiple Regression Analysis To Task: A Review Of Mindware: Tools For Smart Thinking, By Richard Nisbett (2015), Jason Makansi
Taking Multiple Regression Analysis To Task: A Review Of Mindware: Tools For Smart Thinking, By Richard Nisbett (2015), Jason Makansi
Numeracy
Richard Nisbett. 2015. Mindware: Tools for Smart Thinking.(New York, NY: Farrar, Strauss, and Giroux). 336 pp. ISBN: 9780374536244
Nisbett, a psychologist, may not achieve his stated goal of teaching readers to “effortlessly” extend their common sense when it comes to quantitative analysis applied to everyday issues, but his critique of multiple regression analysis (MRA) in the middle chapters of Mindware is worth attention from, and contemplation by, the QL/QR and Numeracy community. While in at least one other source, Nisbett’s critique has been called a “crusade” against MRA, what he really advocates is that it not be used as …
Using Meta-Analysis To Assess Affective Outcomes In A Multi-Course Qr Module Intervention, James Friedrich, Kelley D. Strawn
Using Meta-Analysis To Assess Affective Outcomes In A Multi-Course Qr Module Intervention, James Friedrich, Kelley D. Strawn
Numeracy
When quantitative reasoning(QR) interventions share a common hypothesis or goal, a promising approach for evaluation involves integrating separate analyses through the use of meta-analysis. This paper reports an assessment of a module-based QR intervention distributed across 20 courses at a single institution. Topics and participating courses were diverse, including arts & humanities, quantitative behavioral sciences, and natural sciences & mathematics groupings, but all addressed the shared affective goals of reducing student QR self-doubt and increasing appreciation for QR value and utility. With a local framework to guide module development, we assess these outcomes using reliable self-report measures in a pre-post …
Statistical Learning Of Biomedical Non-Stationary Signals And Quality Of Life Modeling, Mahdi Goudarzi
Statistical Learning Of Biomedical Non-Stationary Signals And Quality Of Life Modeling, Mahdi Goudarzi
USF Tampa Graduate Theses and Dissertations
Statistical learning is a set of tools for modeling and understanding complex datasets. It is a recently developed area in statistics and blends with parallel developments in computer science and, in particular, machine learning.
The classification of biomedical non-stationary signals such as Electroencephalogram (EEG) is always a challenging problem due to their complexity. The low spatial resolution on the scalp, curse of dimensionality, poor signal-to-noise ratio are disadvantages of working with biomedical signals. EEG signals are unstructured data which needs preprocessing steps to extract informative features which are measurable and predictive. In the first two chapters of this dissertation, EEG …
Probabilistic And Statistical Prediction Models For Alzheimer’S Disease And Statistical Analysis Of Global Warming, Maryam Ibrahim Habadi
Probabilistic And Statistical Prediction Models For Alzheimer’S Disease And Statistical Analysis Of Global Warming, Maryam Ibrahim Habadi
USF Tampa Graduate Theses and Dissertations
The importance and applicability of data-driven statistical models have increased significantly. This current study, we have utilized statistical techniques in interdisciplinary research, including environmental and health.
Environmentally, global warming is considered one of the critical issues facing our planet. It is the increase in average global temperatures caused mostly by increases in Carbon Dioxide CO2. The excessive rise of carbon dioxide from the average level as the side effect of the industrial revolution has a significant impact on blocking the heat and increase the temperature within the Earth’s atmosphere. Based on the record of total CO2 emissions …
Essays On Time Series And Machine Learning Techniques For Risk Management, Michael Kotarinos
Essays On Time Series And Machine Learning Techniques For Risk Management, Michael Kotarinos
USF Tampa Graduate Theses and Dissertations
The Capital Asset Pricing Model combined with the Sharpe ratio is a standard method for choosing assets for selection in a portfolio. However, this method has many structural issues and was designed for a time when high dimensional computing was in its infancy. An alternative to these methods using a mix of Multi-Level Time Series Clustering, the MACBETH algorithm and traditional time series techniques was constructed that minimized data loss and allow for customized portfolio construction for investors with different risk profiles and specialized investment needs. It was shown that these methods are adaptable to cloud computing environments and allow …
Exploring The Behavior Of Model Fit Criteria In The Bayesian Approximate Measurement Invariance: A Simulation Study, Abeer Atallah S. Alamri
Exploring The Behavior Of Model Fit Criteria In The Bayesian Approximate Measurement Invariance: A Simulation Study, Abeer Atallah S. Alamri
USF Tampa Graduate Theses and Dissertations
Measurement invariance (MI) is conducted to ensure that differences found in the results of group comparisons are due to true substantive differences and not methodological artifacts. Previous cross-cultural and cross-national studies with large number of groups showed that the advanced measurement invariance level was rarely held when utilizing the traditional (frequentist) MI approach. The Bayesian approximate measurement invariance (BAMI) was introduced to override the traditional MI strict assumption, because trivial non-invariance in parameters across groups is allowed. Although the concept of the BAMI, which has been utilized since 2013, was incorporated into the context of structural equation modeling, there is …
Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall
Numeracy And Social Justice: A Wide, Deep, And Longstanding Intersection, Kira Hamman, Victor Piercey, Samuel L. Tunstall
Numeracy
We discuss the connection between the numeracy and social justice movements both in historical context and in its modern incarnation. The intersection between numeracy and social justice encompasses a wide variety of disciplines and quantitative topics, but within that variety there are important commonalities. We examine the importance of sound quantitative measures for understanding social issues and the necessity of interdisciplinary collaboration in this work. Particular reference is made to the papers in the first part of the Numeracy special collection on social justice, which appear in this issue.