Analyzing Competitive Balance In Professional Sport,
2020
University of Connecticut - Storrs
Analyzing Competitive Balance In Professional Sport, Kevin Alwell
Honors Scholar Theses
In this paper we review several measures to statistically analyze competitive balance and report which leagues have a wider variance of performance amongst its competitors. Each league seeks to maintain high levels of parity, making matches and overall season more unpredictable and appealing to the general audience. Here we quantify competitive advantage across major sports leagues in numbers using several statistical methods in order for leagues to optimize their revenue.
Point Process Modelling Of Objects In The Star Formation Complexes Of The M33 Galaxy,
2020
The University of Western Ontario
Point Process Modelling Of Objects In The Star Formation Complexes Of The M33 Galaxy, Dayi Li
Electronic Thesis and Dissertation Repository
In this thesis, Gibbs point process (GPP) models are constructed to study the spatial distribution of objects in the star formation complexes of the M33 galaxy. The GPP models circumvent the limitations of the two-point correlation function employed in the current astronomy literature by naturally accounting for the inhomogeneous distribution of these objects. The spatial distribution of these objects serves as a sensitive probe in understanding the star formation process, which is crucial in understanding the formation of galaxies and the Universe. The objects under study include the CO filament structure, giant molecular clouds (GMCs) and young stellar cluster candidates …
483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods,
2020
SUNY Geneseo
483— Effectiveness Of Mmr Vaccination In Orthodox Jewish Neighborhoods, Meenu Mundackal
GREAT Day Posters
Measles is a highly contagious disease, where large outbreaks arise by direct contact between susceptible (unvaccinated) and infectious individuals. Many Orthodox Jewish neighborhoods were affected by measles from 2018-2019. To quantify the vaccination effort on this susceptible population, a retrospective analysis was used to study the NYC and Rockland County populations using a differential equations model. A subsequent model, known as a realistically-structured network model, studied only the NYC population, in relation to typical household size. Vaccination strategies were applied to three cohorts: unvaccinated family members, members with 1 prior MMR dose, and members with 2 prior MMR doses. The …
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola,
2020
SUNY Geneseo
484— Modeling Social Distancing Methods And Their Effectiveness In Combating The Spread Of Ebola, Rachel Fair
GREAT Day Posters
Ebola Virus Disease (EVD) is a rare but severe disease that is transmitted among humans through direct-contact with, and close proximity to, infected bodily fluids. From 2014-16, West Africa experienced the largest Ebola outbreak ever recorded, infecting over 28,000 people, and killing over 11,000. Although the symptoms of EVD are treatable, the disease can be extremely deadly, with an average of 50% EVD cases resulting in fatality. In areas where healthcare is scarce and vaccinations are not readily available, the practices of social distancing and self-quarantining have been shown to be highly effective in combating the spread of EVD. To …
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach,
2020
Southern Methodist University
Demand Forecasting In Wholesale Alcohol Distribution: An Ensemble Approach, Tanvi Arora, Rajat Chandna, Stacy Conant, Bivin Sadler, Robert Slater
SMU Data Science Review
In this paper, historical data from a wholesale alcoholic beverage distributor was used to forecast sales demand. Demand forecasting is a vital part of the sale and distribution of many goods. Accurate forecasting can be used to optimize inventory, improve cash ow, and enhance customer service. However, demand forecasting is a challenging task due to the many unknowns that can impact sales, such as the weather and the state of the economy. While many studies focus effort on modeling consumer demand and endpoint retail sales, this study focused on demand forecasting from the distributor perspective. An ensemble approach was applied …
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring,
2020
Kennesaw State University
Data-Driven Investment Decisions In P2p Lending: Strategies Of Integrating Credit Scoring And Profit Scoring, Yan Wang
Doctor of Data Science and Analytics Dissertations
In this dissertation, we develop and discuss several loan evaluation methods to guide the investment decisions for peer-to-peer (P2P) lending. In evaluating loans, credit scoring and profit scoring are the two widely utilized approaches. Credit scoring aims at minimizing the risk while profit scoring aims at maximizing the profit. This dissertation addresses the strengths and weaknesses of each scoring method by integrating them in various ways in order to provide the optimal investment suggestions for different investors. Before developing the methods for loan evaluation at the individual level, we applied the state-of-the-art method called the Long Short Term Memory (LSTM) …
Interdependence Across Foreign Exchange Rate Markets- A Mixed Copula Approach,
2020
Western Kentucky University
Interdependence Across Foreign Exchange Rate Markets- A Mixed Copula Approach, Richard Adjei-Boateng
Masters Theses & Specialist Projects
The purpose of this thesis is to study the dependence structure of exchange rate pairs using a mixture of copula as opposed to a single copula approach. Mixed copula models have the ability to generate dependence structures that do not belong to existing copula families. The flexibility in choosing component copulas in this mixture model aids the construction of a system that is simultaneously parsimonious and flexible enough to generate most dependence patterns in exchange rate data. Furthermore, the method of mixture copulas facilitates the separation of both the structure and degree of dependence, concepts that are respectively embodied in …
Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft,
2020
University of South Carolina
Boom Or Bust: Examining The Relationship Between High School Recruiting Rankings And The Nfl Draft, Nicholas E. Tice
Senior Theses
The goal of this thesis is to model the probability of a high school football player’s chance of being drafted based on information taken from their recruiting profile. The response variable is binary and defined as drafted (1) or undrafted (0). The independent variables were collected by scraping data from the recruiting websites including height, weight, position, hometown, recruiting grade and other socioeconomic factors based on the player’s high school. 247Sports and ESPN were the two recruiting services used and compared in this study. Because of the binary nature of the dependent variable, logistic regression and decision trees were chosen …
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals,
2020
Western Kentucky University
A Monte Carlo Analysis Of Standard Error-Based Methods For Computing Confidence Intervals, Elayna Wichert
Masters Theses & Specialist Projects
The objective of this study is to empirically test existing techniques to calculate the likely range of values for a Classical Test Theory true score given an observed score. The traditional method for forming these confidence intervals has used the standard error of measurement (SEM) as the basis for this confidence interval. An alternate equation, the standard error of estimate (SEE), has been recommended in place of the SEM for this purpose, yet it remains overlooked in the field of psychometrics. It is important that the correct equation be used in various applications in personnel psychology. Monte Carlo analyses were …
Bayesian Methods For The Assessment Of Reporting Errors For Data-Sparse Population-Periods With Applications To Estimating Mortality,
2020
University of Massachusetts Amherst
Bayesian Methods For The Assessment Of Reporting Errors For Data-Sparse Population-Periods With Applications To Estimating Mortality, Emily Peterson
Doctoral Dissertations
Population level mortality data is often subject to substantial reporting errors due to misclassification of cause of death, misclassification of death status, or age reporting errors. Accuracy of error-prone data sources can be assessed by comparing such data to gold standard data for the same population-period. We present Bayesian methods for assessing the extent of reporting errors across different population-periods and generalizing those to settings where gold-standard data are lacking. Firstly, we investigate misclassification errors of maternal cause of death reporting in civil registration vital statistics data. We use a Bayesian hierarchical bivariate random-walk model to estimate country-year specific sensitivity …
Development Of Gaussian Learning Algorithms For Early Detection Of Alzheimer's Disease,
2020
Florida International University
Development Of Gaussian Learning Algorithms For Early Detection Of Alzheimer's Disease, Chen Fang
FIU Electronic Theses and Dissertations
Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer's Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot …
Inferences For Weibull-Gamma Distribution In Presence Of Partially Accelerated Life Test,
2020
Al-Azhar University - Egypt
Inferences For Weibull-Gamma Distribution In Presence Of Partially Accelerated Life Test, Mahmoud Mansour, M A W Mahmoud Prof., Rashad El-Sagheer
Basic Science Engineering
In this paper, the point at issue is to deliberate point and interval estimations for the parameters of Weibull-Gamma distribution (WGD) using progressively Type-II censored (PROG-II-C) sample under step stress partially accelerated life test (SSPALT) model. The maximum likelihood (ML), Bayes, and four parametric bootstrap methods are used to obtain the point estimations for the distribution parameters and the acceleration factor. Furthermore, the approximate confidence intervals (ACIs), four bootstrap confidence intervals and credible intervals of the estimators have been gotten. The results of Bayes estimators are computed under the squared error loss (SEL) function using Markov Chain Monte Carlo (MCMC) …
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy,
2020
Washington University in St. Louis
Measuring Localization Confidence For Quantifying Accuracy And Heterogeneity In Single-Molecule Super-Resolution Microscopy, Hesam Mazidi, Tianben Ding, Arye Nehorai, Matthew D. Lew
Electrical & Systems Engineering Publications and Presentations
We present a computational method, termed Wasserstein-induced flux (WIF), to robustly quantify the accuracy of individual localizations within a single-molecule localization microscopy (SMLM) dataset without ground- truth knowledge of the sample. WIF relies on the observation that accurate localizations are stable with respect to an arbitrary computational perturbation. Inspired by optimal transport theory, we measure the stability of individual localizations and develop an efficient optimization algorithm to compute WIF. We demonstrate the advantage of WIF in accurately quantifying imaging artifacts in high-density reconstruction of a tubulin network. WIF represents an advance in quantifying systematic errors with unknown and complex distributions, …
An Automatic Interaction Detection Hybrid Model For Bankcard Response Classification,
2020
Kennesaw State University
An Automatic Interaction Detection Hybrid Model For Bankcard Response Classification, Yan Wang, Sherry Ni, Brian Stone
Published and Grey Literature from PhD Candidates
Data mining techniques have numerous applications in bankcard response modeling. Logistic regression has been used as the standard modeling tool in the financial industry because of its almost always desirable performance and its interpretability. In this paper, we propose a hybrid bankcard response model, which integrates decision tree-based chi-square automatic interaction detection (CHAID) into logistic regression. In the first stage of the hybrid model, CHAID analysis is used to detect the possible potential variable interactions. Then in the second stage, these potential interactions are served as the additional input variables in logistic regression. The motivation of the proposed hybrid model …
A Two-Stage Hybrid Model By Using Artificial Neural Networks As Feature Construction Algorithms,
2020
Kennesaw State University
A Two-Stage Hybrid Model By Using Artificial Neural Networks As Feature Construction Algorithms, Yan Wang, Sherry Ni, Brian Stone
Published and Grey Literature from PhD Candidates
We propose a two-stage hybrid approach with neural networks as the new feature construction algorithms for bankcard response classifications. The hybrid model uses a very simple neural network structure as the new feature construction tool in the first stage, then the newly created features are used as the additional input variables in logistic regression in the second stage. The model is compared with the traditional one-stage model in credit customer response classification. It is observed that the proposed two-stage model outperforms the one-stage model in terms of accuracy, the area under the ROC curve, and KS statistic. By creating new …
Predicting Class-Imbalanced Business Risk Using Resampling, Regularization, And Model Ensembling Algorithms,
2020
Kennesaw State University
Predicting Class-Imbalanced Business Risk Using Resampling, Regularization, And Model Ensembling Algorithms, Yan Wang, Sherry Ni
Published and Grey Literature from PhD Candidates
We aim at developing and improving the imbalanced business risk modeling via jointly using proper evaluation criteria, resampling, cross-validation, classifier regularization, and ensembling techniques. Area Under the Receiver Operating Characteristic Curve (AUC of ROC) is used for model comparison based on 10-fold cross-validation. Two undersampling strategies including random undersampling (RUS) and cluster centroid undersampling (CCUS), as well as two oversampling methods including random oversampling (ROS) and Synthetic Minority Oversampling Technique (SMOTE), are applied. Three highly interpretable classifiers, including logistic regression without regularization (LR), L1-regularized LR (L1LR), and decision tree (DT) are implemented. Two ensembling techniques, including Bagging and Boosting, are …
A Xgboost Risk Model Via Feature Selection And Bayesian Hyper-Parameter Optimization,
2020
Kennesaw State University
A Xgboost Risk Model Via Feature Selection And Bayesian Hyper-Parameter Optimization, Yan Wang, Sherry Ni
Published and Grey Literature from PhD Candidates
This paper aims to explore models based on the extreme gradient boosting (XGBoost) approach for business risk classification. Feature selection (FS) algorithms and hyper-parameter optimizations are simultaneously considered during model training. The five most commonly used FS methods including weight by Gini, weight by Chi-square, hierarchical variable clustering, weight by correlation, and weight by information are applied to alleviate the effect of redundant features. Two hyper-parameter optimization approaches, random search (RS) and Bayesian tree-structuredParzen Estimator (TPE), are applied in XGBoost. The effect of different FS and hyper-parameter optimization methods on the model performance are investigated by the Wilcoxon Signed Rank …
Quantitative Model For Setting Manufacturer's Suggested Retail Price,
2020
Southern Methodist University
Quantitative Model For Setting Manufacturer's Suggested Retail Price, Peter Byrd, Jonathan Knowles, Dmitry Andreev, Jacob Turner, Brian Mente, Laroux Wallace
SMU Data Science Review
In this paper, we present a quantitative approach to model the manufacturer’s suggested retail price (MSRP) for children’s doll- houses and establish relationships among key features that contribute most to establishing MSRP. Determination of the MSRP is a critical step in how consumers respond with their wallets when purchasing an item. KidKraft, a global leader in toys and juvenile products, sets MSRP subjectively using product experts. The process is arduous and time consuming requiring the focus of specialized resources and knowledge of the interaction between key attributes and their impact on consumer value. An accurate prediction of MSRP during the …
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis,
2020
Murray State University
Evaluating An Ordinal Output Using Data Modeling, Algorithmic Modeling, And Numerical Analysis, Martin Keagan Wynne Brown
Murray State Theses and Dissertations
Data and algorithmic modeling are two different approaches used in predictive analytics. The models discussed from these two approaches include the proportional odds logit model (POLR), the vector generalized linear model (VGLM), the classification and regression tree model (CART), and the random forests model (RF). Patterns in the data were analyzed using trigonometric polynomial approximations and Fast Fourier Transforms. Predictive modeling is used frequently in statistics and data science to find the relationship between the explanatory (input) variables and a response (output) variable. Both approaches prove advantageous in different cases depending on the data set. In our case, the data …
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not,
2020
Misericordia University
Power Analysis On A Pilot Study Of The Caloric Intake Of Children Helping Prepare Meals Versus Children Not, Danielle Clifford
Student Research Poster Presentations 2020
The purpose of this analysis is to determine the sample size needed for a study that will be used to discover if there is a difference in the caloric intake of children who help with meal preparation and children who do not help with meal preparation.
