Applied Statistics Commons^™

Modeling And Mapping Location-Dependent Human Appearance, Zachary Bessinger

Theses and Dissertations--Computer Science

Human appearance is highly variable and depends on individual preferences, such as fashion, facial expression, and makeup. These preferences depend on many factors including a person's sense of style, what they are doing, and the weather. These factors, in turn, are dependent upon geographic location and time. In our work, we build computational models to learn the relationship between human appearance, geographic location, and time. The primary contributions are a framework for collecting and processing geotagged imagery of people, a large dataset collected by our framework, and several generative and discriminative models that use our dataset to learn the relationship …

Occurrence And Attributes Of Two Echinoderm-Bearing Faunas From The Upper Mississippian (Chesterian; Lower Serpukhovian) Ramey Creek Member, Slade Formation, Eastern Kentucky, U.S.A., Ann Well Harris

Theses and Dissertations--Earth and Environmental Sciences

Well-preserved echinoderm faunas are rare in the fossil record, and when uncovered, understanding their occurrence can be useful in interpreting other faunas. In this study, two such faunas of the same age from separate localities in the shallow-marine Ramey Creek Member of the Slade Formation in the Upper Mississippian (Chesterian) rocks of eastern Kentucky are examined. Of the more than 5,000 fossil specimens from both localities, only 9–34 percent were echinoderms from 3–5 classes. Nine non-echinoderm (8 invertebrate and one vertebrate) classes occurred at both localities, but of these, bryozoans, brachiopods and sponges dominated. To understand the attributes of both …

Accounting For Matching Uncertainty In Photographic Identification Studies Of Wild Animals, Amanda R. Ellis

Theses and Dissertations--Statistics

I consider statistical modelling of data gathered by photographic identification in mark-recapture studies and propose a new method that incorporates the inherent uncertainty of photographic identification in the estimation of abundance, survival and recruitment. A hierarchical model is proposed which accepts scores assigned to pairs of photographs by pattern recognition algorithms as data and allows for uncertainty in matching photographs based on these scores. The new models incorporate latent capture histories that are treated as unknown random variables informed by the data, contrasting past models having the capture histories being fixed. The methods properly account for uncertainty in the matching …

The Family Of Conditional Penalized Methods With Their Application In Sufficient Variable Selection, Jin Xie

Theses and Dissertations--Statistics

When scientists know in advance that some features (variables) are important in modeling a data, then these important features should be kept in the model. How can we utilize this prior information to effectively find other important features? This dissertation is to provide a solution, using such prior information. We propose the Conditional Adaptive Lasso (CAL) estimates to exploit this knowledge. By choosing a meaningful conditioning set, namely the prior information, CAL shows better performance in both variable selection and model estimation. We also propose Sufficient Conditional Adaptive Lasso Variable Screening (SCAL-VS) and Conditioning Set Sufficient Conditional Adaptive Lasso Variable …

Mixtures-Of-Regressions With Measurement Error, Xiaoqiong Fang

Theses and Dissertations--Statistics

Finite Mixture model has been studied for a long time, however, traditional methods assume that the variables are measured without error. Mixtures-of-regression model with measurement error imposes challenges to the statisticians, since both the mixture structure and the existence of measurement error can lead to inconsistent estimate for the regression coefficients. In order to solve the inconsistency, We propose series of methods to estimate the mixture likelihood of the mixtures-of-regressions model when there is measurement error, both in the responses and predictors. Different estimators of the parameters are derived and compared with respect to their relative efficiencies. The simulation results …

Improved Standard Error Estimation For Maintaining The Validities Of Inference In Small-Sample Cluster Randomized Trials And Longitudinal Studies, Whitney Ford Tanner

Theses and Dissertations--Epidemiology and Biostatistics

Data arising from Cluster Randomized Trials (CRTs) and longitudinal studies are correlated and generalized estimating equations (GEE) are a popular analysis method for correlated data. Previous research has shown that analyses using GEE could result in liberal inference due to the use of the empirical sandwich covariance matrix estimator, which can yield negatively biased standard error estimates when the number of clusters or subjects is not large. Many techniques have been presented to correct this negative bias; However, use of these corrections can still result in biased standard error estimates and thus test sizes that are not consistently at their …

Old English Character Recognition Using Neural Networks, Sattajit Sutradhar

College of Graduate Studies: Theses & 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 …

A Primer On Noise-Induced Transitions In Applied Dynamical Systems, Eric Forgoston, Richard O. Moore

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

Noise plays a fundamental role in a wide variety of physical and biological dynamical systems. It can arise from an external forcing or due to random dynamics internal to the system. It is well established that even weak noise can result in large behavioral changes such as transitions between or escapes from quasi-stable states. These transitions can correspond to critical events such as failures or extinctions that make them essential phenomena to understand and quantify, despite the fact that their occurrence is rare. This article will provide an overview of the theory underlying the dynamics of rare events for stochastic …

A Probability Model For Strategic Bidding On The Price Is Right, Paul H. Kvam

Department of Math & Statistics Faculty Publications

The TV game show “The Price is Right” features a bidding auction called “Contestants’ Row” that rewards the player (out of 4) who bids closest to an item’s value, without overbidding. This paper considers ways in which players can maximize a winning probability based on the player's bidding order. We consider marginal strategies in which players assume opponents are bidding individually perceived values of the merchandise. Based on preceding bids of others, players have information available to create strategies. We consider conditional strategies in which players adjust bids knowing other players are using strategies. The last bidder has a large …

A Model To Predict Concentrations And Uncertainty For Mercury Species In Lakes, Ashley Hendricks

Dissertations, Master's Theses and Master's Reports

To increase understanding of mercury cycling, a seasonal mass balance model was developed to predict mercury concentrations in lakes and fish. Results indicate that seasonality in mercury cycling is significant and is important for a northern latitude lake. Models, when validated, have the potential to be used as an alternative to measurements; models are relatively inexpensive and are not as time intensive. Previously published mercury models have neglected to perform a thorough validation. Model validation allows for regulators to be able to make more informed, confident decisions when using models in water quality management. It is critical to quantify uncertainty; …

The Use Of Item Response Theory In Survey Methodology: Application In Seat Belt Data, Mark K. Ledbetter, Norou Diawara, Bryan E. Porter

Mathematics & Statistics Faculty Publications

Problem: Several approaches to analyze survey data have been proposed in the literature. One method that is not popular in survey research methodology is the use of item response theory (IRT). Since accurate methods to make prediction behaviors are based upon observed data, the design model must overcome computation challenges, but also consideration towards calibration and proficiency estimation. The IRT model deems to be offered those latter options. We review that model and apply it to an observational survey data. We then compare the findings with the more popular weighted logistic regression. Method: Apply IRT model to the observed data …

Car Insurance Rate-Making With An Eye Toward The Future, Stephen Howard

Williams Honors College, Honors Research Projects

For my project, I investigated car insurance rate-making. I took an in-depth look at the car insurance industry. I also studied driverless cars, and the expected timeline surrounding them. I also took a look at how driverless cars are expected to change the car insurance industry in the coming decades. In general, what I found did not surprise me. I did, however, glean insight from various opinions that I read about how the insurance industry is likely to change. Change of some sort in the car insurance industry is sure to come, with companies likely to become much more multi-faceted. …

Campus Climate Sexual Assault Survey (2015) Analysis, Felicia Rosin

Williams Honors College, Honors Research Projects

The issue of sexual assault has garnered widespread attention in recent years, as is evident by the growing number of high-profile cases and mainstream social movements. With this increasingly bright spotlight, it is no surprise that The University of Akron has interest in improving the sexual violence education programs offered to students. In 2015, the university conducted a survey to gather information on the campus climate surrounding sexual assault. This analysis dives into a deeper analysis of the data gathered in an attempt to pinpoint areas that require the university’s attention. The analysis covers topics identified by Dean of Students …

A Review Of The Utility Of Bayesian Network Models, Luke Magyar

Williams Honors College, Honors Research Projects

Bayesian Networks are probabilistic models built from conditional probability tables that relate two observable instances to one another in parent-child fashion. The networks’ strength lies in their ability to use inferential logic to make likelihood assessments about a parent node based on an observation of its child. Additionally, they make it very easy to combine quantitative data with qualitative knowledge from industry experts. These abilities make them very attractive for use as formulation tools in the paint and rubber industries. Paint and rubber formulation has long proven to be a challenging task because companies have a difficult time compiling the …

Decision Trees: Predicting Future Losses For Insurance Data, Amanda Lahrmann

Williams Honors College, Honors Research Projects

Big data is a term that has come to the spotlight for companies within recent years. Data analysis and business intelligence have become prominent sectors of companies and agencies. But what is big data? How has it impacted large companies and agencies? Why must it be embraced?

The best way to approach utilizing a big data set is to establish a question to answer. For this data set, the question that must be answered is “What variables cause a loss to occur?” To answer this question, first, we must understand what is meant by a “loss”, and take a look …

Penalized Mixed-Effects Ordinal Response Models For High-Dimensional Genomic Data In Twins And Families, Amanda E. Gentry

Theses and Dissertations

The Brisbane Longitudinal Twin Study (BLTS) was being conducted in Australia and was funded by the US National Institute on Drug Abuse (NIDA). Adolescent twins were sampled as a part of this study and surveyed about their substance use as part of the Pathways to Cannabis Use, Abuse and Dependence project. The methods developed in this dissertation were designed for the purpose of analyzing a subset of the Pathways data that includes demographics, cannabis use metrics, personality measures, and imputed genotypes (SNPs) for 493 complete twin pairs (986 subjects.) The primary goal was to determine what combination of SNPs and …

Joint Analysis Of Multiple Phenotypes In Association Studies, Xiaoyu Liang

Dissertations, Master's Theses and Master's Reports

Genome-wide association studies (GWAS) have become a very effective research tool to identify genetic variants of underlying various complex diseases. In spite of the success of GWAS in identifying thousands of reproducible associations between genetic variants and complex disease, in general, the association between genetic variants and a single phenotype is usually weak. It is increasingly recognized that joint analysis of multiple phenotypes can be potentially more powerful than the univariate analysis, and can shed new light on underlying biological mechanisms of complex diseases. Therefore, developing statistical methods to test for genetic association with multiple phenotypes has become increasingly important. …

Step-Selection Functions For Modeling Animal Movement -- Case Study: African Buffalo, Maia Adar

CMC Senior Theses

Understanding what factors influence wildlife movement allows landscape planners to make informed decisions that benefit both animals and humans. New quantitative methods, such as step-selection functions, provide valuable objective analyses of wildlife connectivity. This paper provides a framework for creating a step-selection function and demonstrates its use in a case study. The first section provides a general introduction about wildlife connectivity research. The second section explains the math behind the step-selection function using a simple example. The last section gives the results of a step-selection model for African buffalo in the Kavango Zambezi Transfrontier Conservation Area. Buffalo were found to …

Some New And Generalized Distributions Via Exponentiation, Gamma And Marshall-Olkin Generators With Applications, Hameed Abiodun Jimoh

College of Graduate Studies: Theses & 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.

Wildfire Emissions In The Context Of Global Change And The Implications For Mercury Pollution, Aditya Kumar

Dissertations, Master's Theses and Master's Reports

Wildfires are episodic disturbances that exert a significant influence on the Earth system. They emit substantial amounts of atmospheric pollutants, which can impact atmospheric chemistry/composition and the Earth’s climate at the global and regional scales. This work presents a collection of studies aimed at better estimating wildfire emissions of atmospheric pollutants, quantifying their impacts on remote ecosystems and determining the implications of 2000s-2050s global environmental change (land use/land cover, climate) for wildfire emissions following the Intergovernmental Panel on Climate Change (IPCC) A1B socioeconomic scenario.

A global fire emissions model is developed to compile global wildfire emission inventories for major atmospheric …