Stochastic Analysis And Statistical Inference For Seir Models Of Infectious Diseases, Andrés Ríos-Gutiérrez, Viswanathan Arunachalam, Anuj Mubayi
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Direct Questioning Of Sensitive Topics In Public Health Studies: A Simulation Study, 2020 LSU Health Sciences Center, School of Public Health, Biostatistics Program
Direct Questioning Of Sensitive Topics In Public Health Studies: A Simulation Study, Jessica K. Fox, Evrim Oral
Annual Symposium on Biomathematics and Ecology Education and Research
No abstract provided.
Applying The Data: Predictive Analytics In Sport, 2020 University of Washington, Tacoma
Applying The Data: Predictive Analytics In Sport, Anthony Teeter, Margo Bergman
Access*: Interdisciplinary Journal of Student Research and Scholarship
The history of wagering predictions and their impact on wide reaching disciplines such as statistics and economics dates to at least the 1700’s, if not before. Predicting the outcomes of sports is a multibillion-dollar business that capitalizes on these tools but is in constant development with the addition of big data analytics methods. Sportsline.com, a popular website for fantasy sports leagues, provides odds predictions in multiple sports, produces proprietary computer models of both winning and losing teams, and provides specific point estimates. To test likely candidates for inclusion in these prediction algorithms, the authors developed a computer model ...
Task Interrupted By A Poisson Process, 2020 University of Windsor
Task Interrupted By A Poisson Process, Jarrett Christopher Nantais
We consider a task which has a completion time T (if not interrupted), which is a random variable with probability density function (pdf) f(t), t>0. Before it is complete, the task may be interrupted by a Poisson process with rate lambda. If that happens, then the task must begin again, with the same completion time random variable T, but with a potentially different realization. These interruptions can reoccur, until eventually the task is finished, with a total time of W. In this paper, we will find the Laplace Transform of W in several special cases.
Maximum Entropy Classification For Record Linkage, 2020 University of Alabama
Maximum Entropy Classification For Record Linkage, Danhyang Lee, Li-Chun Zhang, Jae Kwang Kim
By record linkage one joins records residing in separate files which are believed to be related to the same entity. In this paper we approach record linkage as a classification problem, and adapt the maximum entropy classification method in text mining to record linkage, both in the supervised and unsupervised settings of machine learning. The set of links will be chosen according to the associated uncertainty. On the one hand, our framework overcomes some persistent theoretical flaws of the classical approach pioneered by Fellegi and Sunter (1969); on the other hand, the proposed algorithm is scalable and fully automatic, unlike ...
Snackjack: A Toy Model Of Blackjack, 2020 University of Utah
Snackjack: A Toy Model Of Blackjack, Stewart N. Ethier, Jiyeon Lee
UNLV Gaming Research & Review Journal
Snackjack is a highly simplified version of blackjack that was proposed by Ethier (2010) and given its name by Epstein (2013). The eight-card deck comprises two aces, two deuces, and four treys, with aces having value either 1 or 4, and deuces and treys having values 2 and 3, respectively. The target total is 7 (vs. 21 in blackjack), and ace-trey is a natural. The dealer stands on 6 and 7, including soft totals, and otherwise hits. The player can stand, hit, double, or split, but split pairs receive only one card per paircard (like split aces in blackjack), and ...
Uniform Random Variate Generation With The Linear Congruential Method, 2020 University of North Florida
Uniform Random Variate Generation With The Linear Congruential Method, Joseph Free
PANDION: The Osprey Journal of Research and Ideas
This report considers the issue of using a speciﬁc linear congruential generator (LCG) to create random variates from the uniform(0,1) distribution. The LCG is used to generate multiple samples of pseudo-random numbers and statistical computation techniques are used to assess whether those samples could have resulted from a uniform(0,1) distribution. Source code is included with this report in the appendix along with annotations.
Lectures On Mathematical Computing With Python, 2020 Portland State University
Lectures On Mathematical Computing With Python, Jay Gopalakrishnan
PDXOpen: Open Educational Resources
This open resource is a collection of class activities for use in undergraduate courses aimed at teaching mathematical computing, and computational thinking in general, using the python programming language. It was developed for a second-year course (MTH 271) revamped for a new undergraduate program in data science at Portland State University. The activities are designed to guide students' use of python modules effectively for scientific computation, data analysis, and visualization.
If you are an instructor adopting or adapting this open educational resource, please help us understand your use by filling out this form
Effect Of Predictor Dependence On Variable Selection For Linear And Log-Linear Regression, 2020 University of Arkansas, Fayetteville
Effect Of Predictor Dependence On Variable Selection For Linear And Log-Linear Regression, Apu Chandra Das
Theses and Dissertations
We propose a Bayesian approach to the Dirichlet-Multinomial (DM) regression model, which uses horseshoe, Laplace, and horseshoe plus priors for shrinkage and selection. The Dirichlet-Multinomial model can be used to find the significant association between a set of available covariates and taxa for a microbiome sample. We incorporate the covariates in a log-linear regression framework. We design a simulation study to make a comparison among the performance of the three shrinkage priors in terms of estimation accuracy and the ability to detect true signals. Our results have clearly separated the performance of the three priors and indicated that the horseshoe ...
Data Integration By Combining Big Data And Survey Sample Data For Finite Population Inference, 2020 Iowa State University
Data Integration By Combining Big Data And Survey Sample Data For Finite Population Inference, Jae Kwang Kim, Siu-Ming Tam
The statistical challenges in using big data for making valid statistical inference in the finite population have been well documented in literature. These challenges are due primarily to statistical bias arising from under-coverage in the big data source to represent the population of interest and measurement errors in the variables available in the data set. By stratifying the population into a big data stratum and a missing data stratum, we can estimate the missing data stratum by using a fully responding probability sample, and hence the population as a whole by using a data integration estimator. By expressing the data ...
The Opioid Crisis And Life Expectancy In The U.S., 2020 University of Pennsylvania
The Opioid Crisis And Life Expectancy In The U.S., Gabriel Lozano
Joseph Wharton Scholars
Since the 1990s, when opioids started to be grossly over-prescribed, almost 450,000 people have died as a direct result of opioid abuse in the United States. This study analyzes the role the opioid crisis has in the decreasing life expectancy in the United States, a troubling trend given the enormous and growing national healthcare expenditure. Employing a multiple decrement model and national life expectancy tables, this paper removes the opioid-related mortality and develops a new life expectancy model. The actuarial analysis of the observed and estimated life expectancies reveals the impact of opioid-related deaths: overall, U.S persons are ...
Life And Death: Quantifying The Risk Of Heart Disease With Machine Learning, 2020 University of Northern Iowa
Life And Death: Quantifying The Risk Of Heart Disease With Machine Learning, Jack Scott Glienke
Honors Program Theses
Coronary heart disease has long been a key area of focus in the discussion of public health. As such, numerous studies have been conducted throughout history with the sole intention of identifying risk factors leading to the onset of cardiovascular conditions. A plethora of statistical procedures can be used to identify an individual’s risk of developing heart disease, yet regression models tend to be the default tool used by researchers. Using the data obtained from the most influential cardiovascular study to date, the Framingham Heart Study, this analysis uses machine learning techniques to generate and test the predictive power ...
On Arnold–Villasenor Conjectures For Characterizaing Exponential Distribution Based On Sample Of Size Three, 2020 The University of Texas Rio Grande Valley
On Arnold–Villasenor Conjectures For Characterizaing Exponential Distribution Based On Sample Of Size Three, George Yanev
Mathematical and Statistical Sciences Faculty Publications and Presentations
Arnold and Villasenor  obtain a series of characterizations of the exponential distribution based on random samples of size two. These results were already applied in constructing goodness-of-fit tests. Extending the techniques from , we prove some of Arnold and Villasenor’s conjectures for samples of size three. An example with simulated data is discussed.
Personal Foul: How Head Trauma And The Insurance Industry Are Threatening Sports, 2020 Liberty University
Personal Foul: How Head Trauma And The Insurance Industry Are Threatening Sports, Zachary Cooler
Senior Honors Theses
This thesis will investigate the growing problem of head trauma in contact sports like football, hockey, and soccer through medical studies, implications to the insurance industry, and ongoing litigation. The thesis will investigate medical studies that are finding more evidence to support the claim that contact sports players are more likely to receive head trauma symptoms such as memory loss, mood swings, and even Lou Gehrig’s disease in extreme cases. The thesis will also demonstrate that these medical symptoms and monetary losses from medical claims are convincing insurance companies to withdraw insurance coverage for sports leagues, which they are ...
D-Vine Copula Model For Dependent Binary Data, 2020 Old Dominion University
D-Vine Copula Model For Dependent Binary Data, Huihui Lin, N. Rao Chaganty
College of Sciences Posters
High-dimensional dependent binary data are prevalent in a wide range of scientific disciplines. A popular method for analyzing such data is the Multivariate Probit (MP) model. But the MP model sometimes fails even within a feasible range of binary correlations, because the underlying correlation matrix of the latent variables may not be positive definite. In this research, we proposed pair copula models, assuming the dependence between the binary variables is first order autoregressive (AR(1))or equicorrelated structure. Also, when Archimediean copula is used, most paper converted Kendall Tau to corresponding copula parameter, there is no explicit function of Pearson ...
Dice Questions Answered, 2020 Civil Engineering
Dice Questions Answered, Warren Campbell, William P. Dolan
SEAS Faculty Publications
Superstitious discussion of fair and unfair dice has pervaded the tabletop gaming industry since its inception. Many of these are not based on any quantitative data or studies. Consequently, misconceptions have been spread widely. One dice float test video on Youtube currently has 925,000 views (Fisher, 2015a). To combat the flood of misconceptions we investigated the following questions: 1) Are dice cursed? 2) Are D20s (20-sided dice) less fair than D6s (6-sided dice)? 3) Do float tests tell anything about the fairness of dice? 4) Are some dice systems inherently fairer than others? 5) Are density differences or dimensions ...
Doubly Robust Inference When Combining Probability And Non-Probability Samples With High-Dimensional Data, 2020 North Carolina State University
Doubly Robust Inference When Combining Probability And Non-Probability Samples With High-Dimensional Data, Shu Yang, Jae Kwang Kim, Rui Song
Non-probability samples become increasingly popular in survey statistics but may suffer from selection biases that limit the generalizability of results to the target population. We consider integrating a non-probability sample with a probability sample which provides high-dimensional representative covariate information of the target population. We propose a two-step approach for variable selection and finite population inference. In the first step, we use penalized estimating equations with folded-concave penalties to select important variables for the sampling score of selection into the non-probability sample and the outcome model. We show that the penalized estimating equation approach enjoys the selection consistency property for ...
Some New Results On Stochastic Comparisons Of Coherent Systems Using Signatures, 2020 Bu-Ali Sina University
Some New Results On Stochastic Comparisons Of Coherent Systems Using Signatures, Ebrahim Amini-Seresht, Baha-Eldin Khaledi, Subhash C. Kochar
Mathematics and Statistics Faculty Publications and Presentations
We consider coherent systems with independent and identically distributed components. While it is clear that the system’s life will be stochastically larger when the components are replaced with stochastically better components, we show that, in general, similar results may not hold for hazard rate, reverse hazard rate, and likelihood ratio orderings. We find sufficient conditions on the signature vector for these results to hold. These results are combined with other well-known results in the literature to get more general results for comparing two systems of the same size with different signature vectors and possibly with different independent and identically ...
Next-Generation Air Force Weather Metrics Via Bayes Cost Analysis, 2020 Air Force Institute of Technology
Next-Generation Air Force Weather Metrics Via Bayes Cost Analysis, Brandon M. Bailey
Theses and Dissertations
This research proposes a new methodology for U.S. Air Force weather forecast metrics. Military weather forecasters are essentially statistical classifiers. They categorize future conditions into an operationally relevant category based on current data, much like an Artificial Neural Net or Logistic Regression model. There is extensive literature on statistically-based metrics for these types of classifiers. Additionally, in the U.S. Air Force, forecast errors (errors in classification) have quantifiable operational costs and benefits associated with incorrect or correct classification decisions. There is a methodology in the literature, Bayes Cost, which provides a structure for creating statistically rigorous metrics for ...
Teaching A University Course On The Mathematics Of Gambling, 2020 University of Utah
Teaching A University Course On The Mathematics Of Gambling, Stewart N. Ethier, Fred M. Hoppe
UNLV Gaming Research & Review Journal
Courses on the mathematics of gambling have been offered by a number of colleges and universities, and for a number of reasons. In the past 15 years, at least seven potential textbooks for such a course have been published. In this article we objectively compare these books for their probability content, their gambling content, and their mathematical level, to see which ones might be most suitable, depending on student interests and abilities. This is not a book review (e.g., none of the books is recommended over others) but rather an essay offering advice about which topics to include in ...