Statistical Models Commons^™

Using Multivariate Statistical Techniques To Aid In A Sports Index Construction, Tiffany Kelly

Mathematics Colloquium Series

Within a quantitative career, you are/will soon be challenged to create an overall value to explain a situational status. For example, socio-economic status, well-being, and in this specific example, happiness among sports fans. This talk seeks to discuss my previous work developed out from student research performed at NSU in its application to my first project for ESPN Sports Analytics, the College Football Fan Happiness Index (http://es.pn/2vmParA) . I will dive into the multivariate statistical techniques of principal component analysis and hierarchal clustering to create this happiness index from a slew of variables.

Latent Storm Factors And Their Indicators, Joy D'Andrea

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

Handguns And Hotspots: Spatio- Temporal Models For Gun Violence In Chicago,Il, Shelby Scott

Annual Symposium on Biomathematics and Ecology Education and Research

No abstract provided.

A Geometric Derivation Of The Irwin-Hall Distribution, James E. Marengo, Lucas Stefanic, David L. Farnsworth

Articles

The Irwin-Hall distribution is the distribution of the sum of a finite number of independent identically distributed uniform random variables on the unit interval. Many applications arise since round-off errors have a transformed Irwin-Hall distribution and the distribution supplies spline approximations to normal distributions. We review some of the distribution’s history. The present derivation is very transparent, since it is geometric and explicitly uses the inclusion-exclusion principle. In certain special cases, the derivation can be extended to linear combinations of independent uniform random variables on other intervals of finite length.The derivation adds to the literature about methodologies for finding distributions …

Models And Policies Of Port Carbon Emission Reduction : A Case Study Of The Port Of Dalian, Jiaqiong Zhao

World Maritime University Dissertations

No abstract provided.

Examination And Comparison Of The Performance Of Common Non-Parametric And Robust Regression Models, Gregory F. Malek

Electronic Theses and Dissertations

ABSTRACT

Examination and Comparison of the Performance of Common Non-Parametric and Robust Regression Models

By

Gregory Frank Malek

Stephen F. Austin State University, Masters in Statistics Program,

Nacogdoches, Texas, U.S.A.

[email protected]

This work investigated common alternatives to the least-squares regression method in the presence of non-normally distributed errors. An initial literature review identified a variety of alternative methods, including Theil Regression, Wilcoxon Regression, Iteratively Re-Weighted Least Squares, Bounded-Influence Regression, and Bootstrapping methods. These methods were evaluated using a simple simulated example data set, as well as various real data sets, including math proficiency data, Belgian telephone call data, and faculty …

Camel And Adcirc Storm Surge Models—A Comparative Study, Muhammad K. Akbar, Richard A. Luettich, Jason G. Fleming, Shahrouz K. Aliabadi

Mechanical and Manufacturing Engineering Faculty Research

The Computation and Modeling Engineering Laboratory (CaMEL), an implicit solver-based storm surge model, has been extended for use on high performance computing platforms. An MPI (Message Passing Interface) based parallel version of CaMEL has been developed from the previously existing serial version. CaMEL uses hybrid finite element and finite volume techniques to solve shallow water conservation equations in either a Cartesian or a spherical coordinate system and includes hurricane-induced wind stress and pressure, bottom friction, the Coriolis effect, and tidal forcing. Both semi-implicit and fully-implicit time stepping formulations are available. Once the parallel implementation is properly validated, CaMEL is evaluated …

Thermodynamics Of Coherent Structures Near Phase Transitions, Julia M. Meyer, Ivan Christov

The Summer Undergraduate Research Fellowship (SURF) Symposium

Phase transitions within large-scale systems may be modeled by nonlinear stochastic partial differential equations in which system dynamics are captured by appropriate potentials. Coherent structures in these systems evolve randomly through time; thus, statistical behavior of these fields is of greater interest than particular system realizations. The ability to simulate and predict phase transition behavior has many applications, from material behaviors (e.g., crystallographic phase transformations and coherent movement of granular materials) to traffic congestion. Past research focused on deriving solutions to the system probability density function (PDF), which is the ground-state wave function squared. Until recently, the extent to which …

Using Mountain Snowpack To Predict Summer Water Availability In Semiarid Mountain Watersheds, Rebecca Dawn Garst

Boise State University Theses and Dissertations

In the mountainous landscapes of the western United States, water resources are dominated by snowpack. As temperatures rise in spring and summer, the melting snow produces an increase in river flow levels. Reservoirs are used during this increase to retain surplus water, which is released to supplement growing season water supply once the peak flows decrease to below water demands. Once there is no longer surplus natural flow of water, the water accounting changes – referred to as the day of allocation (DOA), and water previously retained within the reservoir is used to supplement the lower flow levels. The amount …

A Characterization Of A Value Added Model And A New Multi-Stage Model For Estimating Teacher Effects Within Small School Systems, Julie M. Garai

Department of Statistics: Dissertations, Theses, and Student Research

At both the national and state level there is increasing pressure to develop metrics to determine if school systems are meeting educational objectives. All states mandate some form of assessment by standardized tests. One method currently used to model student test scores is Value Added Modeling (VAM), which models student scores as a product of classroom and school environments. One VAM approach is the Tennessee Value Added Assessment System (TVAAS) which models student gains from year to year. Teacher effects are included in this layered model, which estimates the teacher’s added value to a student score through best linear unbiased …

Supervised Classification Using Finite Mixture Copula, Sumen Sen, Norou Diawara

Mathematics & Statistics Faculty Publications

Use of copula for statistical classification is recent and gaining popularity. For example, statistical classification using copula has been proposed for automatic character recognition, medical diagnostic and most recently in data mining. Classical discrimination rules assume normality. But in this data age time, this assumption is often questionable. In fact features of data could be a mixture of discrete and continues random variables. In this paper, mixture copula densities are used to model class conditional distributions. Such types of densities are useful when the marginal densities of the vector of features are not normally distributed and are of a mixed …

Bayesian Model Averaging With Change Points To Assess The Impact Of Vaccination And Public Health Interventions., Esra Kürüm, Joshua L Warren, Cynthia Schuck-Paim, Roger Lustig, Joseph A Lewnard, Rodrigo Fuentes, Christian A W Bruhn, Robert J Taylor, Lone Simonsen, Daniel M Weinberger

Global Health Faculty Publications

Background: Pneumococcal conjugate vaccines (PCVs) prevent invasive pneumococcal disease and pneumonia. However, some low-and middle-income countries have yet to introduce PCV into their immunization programs due, in part, to lack of certainty about the potential impact. Assessing PCV benefits is challenging because specific data on pneumococcal disease are often lacking, and it can be difficult to separate the effects of factors other than the vaccine that could also affect pneumococcal disease rates.

Methods: We assess PCV impact by combining Bayesian model averaging with change-point models to estimate the timing and magnitude of vaccine-associated changes, while controlling for seasonality and other …

Application Of Support Vector Machine Modeling And Graph Theory Metrics For Disease Classification, Jessica M. Rudd

Published and Grey Literature from PhD Candidates

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with …

Testing The Independence Hypothesis Of Accepted Mutations For Pairs Of Adjacent Amino Acids In Protein Sequences, Jyotsna Ramanan, Peter Revesz

School of Computing: Faculty Publications

Evolutionary studies usually assume that the genetic mutations are independent of each other. However, that does not imply that the observed mutations are independent of each other because it is possible that when a nucleotide is mutated, then it may be biologically beneficial if an adjacent nucleotide mutates too. With a number of decoded genes currently available in various genome libraries and online databases, it is now possible to have a large-scale computer-based study to test whether the independence assumption holds for pairs of adjacent amino acids. Hence the independence question also arises for pairs of adjacent amino acids within …

Visualizing Lab And Phenotype Associations Using Phewas And Electronic Health Records, Brenda Emerson, Miriam Goldman, Sahiti Kolli

Honors Projects

As the digitization of patient health records is becoming more common, we are given a great opportunity to analyze these records and hopefully make discoveries about diseases or medicines. Being given large datasets of Electronic Health Records, I and two other students decided to look for novel phenotype associations with mean lab values, look to see whether the presence of a lab had associations with a phenotype, and create an interactive application to visual the associations between labs and phenotypes.

Burden Of Atopic Dermatitis In The United States: Analysis Of Healthcare Claims Data In The Commercial, Medicare, And Medi-Cal Databases, Sulena Shrestha, Raymond Miao, Li Wang, Jingdong Chao, Huseyin Yuce, Wenhui Wei

Publications and Research

Comparative data on the burden of atopic dermatitis (AD) in adults relative to the general population are limited. We performed a large-scale evaluation of the burden of disease among US adults with AD relative to matched non-AD controls, encompassing comorbidities, healthcare resource utilization (HCRU), and costs, using healthcare claims data. The impact of AD disease severity on these outcomes was also evaluated.

Factor Based Statistical Arbitrage In The U.S. Equity Market With A Model Breakdown Detection Process, Seoungbyung Park

Master's Theses (2009 -)

Many researchers have studied different strategies of statistical arbitrage to provide a steady stream of returns that are unrelated to the market condition. Among different strategies, factor-based mean reverting strategies have been popular and covered by many. This thesis aims to add value by evaluating the generalized pairs trading strategy and suggest enhancements to improve out-of-sample performance. The enhanced strategy generated the daily Sharpe ratio of 6.07% in the out-of-sample period from January 2013 through October 2016 with the correlation of -.03 versus S&P 500. During the same period, S&P 500 generated the Sharpe ratio of 6.03%. This thesis is …

Mixture Models For Undiagnosed Prevalent Disease And Interval-Censored Incident Disease: Applications To A Cohort Assembled From Electronic Health Records., Li C Cheung, Qing Pan, Noorie Hyun, Mark Schiffman, Barbara Fetterman, Philip E Castle, Thomas Lorey, Hormuzd A Katki

Epidemiology Faculty Publications

For cost-effectiveness and efficiency, many large-scale general-purpose cohort studies are being assembled within large health-care providers who use electronic health records. Two key features of such data are that incident disease is interval-censored between irregular visits and there can be pre-existing (prevalent) disease. Because prevalent disease is not always immediately diagnosed, some disease diagnosed at later visits are actually undiagnosed prevalent disease. We consider prevalent disease as a point mass at time zero for clinical applications where there is no interest in time of prevalent disease onset. We demonstrate that the naive Kaplan-Meier cumulative risk estimator underestimates risks at early …

Mechanistic Mathematical Models: An Underused Platform For Hpv Research, Marc Ryser, Patti Gravitt, Evan R. Myers

Global Health Faculty Publications

Health economic modeling has become an invaluable methodology for the design and evaluation of clinical and public health interventions against the human papillomavirus (HPV) and associated diseases. At the same time, relatively little attention has been paid to a different yet complementary class of models, namely that of mechanistic mathematical models. The primary focus of mechanistic mathematical models is to better understand the intricate biologic mechanisms and dynamics of disease. Inspired by a long and successful history of mechanistic modeling in other biomedical fields, we highlight several areas of HPV research where mechanistic models have the potential to advance the …

Gridiron-Gurus Final Report: Fantasy Football Performance Prediction, Kyle Tanemura, Michael Li, Erica Dorn, Ryan Mckinney

Computer Science and Software Engineering

Gridiron Gurus is a desktop application that allows for the creation of custom AI profiles to help advise and compete against in a Fantasy Football setting. Our AI are capable of performing statistical prediction of players on both a season long and week to week basis giving them the ability to both draft and manage a fantasy football team throughout a season.