Statistics and Probability Commons^™

Multiclass Classification Using Support Vector Machines, Duleep Prasanna W. Rathgamage Don

College of Graduate Studies: Theses & Dissertations

In this thesis, we discuss different SVM methods for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning of the whole training data set into disjoint subsets that are easily separable. A single prediction performed between two partitions eliminates one or more classes in a single partition, leaving only a reduced number of candidate classes for subsequent steps. The algorithm continues recursively, reducing the number of classes at each step until a final binary decision is made between the last two classes …

Developing, Piloting, And Factor Analysis Of A Brief Survey Tool For Evaluating Food And Composting Behaviors: The Short Composting Survey, Jennie Norton

All Master's Theses

Composting on a university campus may take a variety of forms. Sustainable approaches to waste management can be taught and supported through educational programs, peer-to-peer behavior modeling, and composting program interventions. Although peer-reviewed research on composting interventions is somewhat lacking, student interest in the topic is demonstrated by a range of exploratory senior projects and pilot interventions conducted at colleges across the United States and abroad. The purpose of this study was twofold: conduct an educational compost intervention pilot study and develop a survey tool to measure participant attitudes surrounding food behaviors and composting. The Compost Project pilot study focused …

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 Comparison Of Bridge Deterioration Models, Toktam Naderimoghaddam

College of Graduate Studies: Theses & Dissertations

Predicting how bridges will deteriorate is the key to budgeting financial and personnel resources. Deterioration models exist for specific components of a bridge, but no models exist for the sufficiency rating which is an overall measure of the condition and relevance of a bridge used for determining eligibility for federal funds.

We have 25 years worth of data collected by the Georgia Department of Transportation from 1992 to 2016 about all bridges in the State of Georgia. More precisely, each row in this data set includes the characteristics of each bridge along with the sufficiency rating of that bridge in …

On Comparability Of Bigrassmannian Permutations, John Engbers, Adam Hammett

Mathematics, Statistics and Computer Science Faculty Research and Publications

Let S_n and G_n denote the respective sets of ordinary and bigrassmannian (BG) permutations of order n, and let (G_n,≤) denote the Bruhat ordering permutation poset. We study the restricted poset (B_n,≤), first providing a simple criterion for comparability. This criterion is used to show that that the poset is connected, to enumerate the saturated chains between elements, and to enumerate the number of maximal elements below r fixed elements. It also quickly produces formulas for β(ω) (α(ω), respectively), the number of BG permutations weakly below (weakly above, respectively) a fixed ω ∈ B …

Non-Linear Machine Learning With Active Sampling For Mox Drift Compensation, Tamara Matthews, Muhammad Iqbal, Horacio Gonzalez-Velez

Conference papers

Abstract—Metal oxide (MOX) gas detectors based on SnO2 provide low-cost solutions for real-time sensing of complex gas mixtures for indoor ambient monitoring. With high sensitivity under ideal conditions, MOX detectors may have poor longterm response accuracy due to environmental factors (humidity and temperature) along with sensor aging, leading to calibration drifts. Finding a simple and efficient solution to correct such calibration drifts has been the subject of numerous studies but remains an open problem. In this work, we present an efficient approach to MOX calibration using active and transfer sampling techniques coupled with non-linear machine learning algorithms, namely neural networks, …

Geographic Variations In Antenatal Care Services In Sierra Leone, Eunice Nyambura Chege

Walden Dissertations and Doctoral Studies

Despite antenatal care presenting opportunities to identify and monitor women at risk, use of recommended antenatal care services remains. Barriers preventing use of antenatal services vary between countries, and limited knowledge exists about the link between geographical settings and antenatal service use. The objective of this cross-sectional quantitative study was to explore geographical variations and investigate how social demographic characteristics affect use of antenatal care for women in Sierra Leone using the Andersen behavioral model. The data used were from the 2016 maternal death surveillance report of the whole counrty (N =706). Logistic regression analysis was used to determine the …

The Kumaraswamy Weibull Geometric Distribution With Applications, Mahdi Rasekhi, Morad Alizadeh, Gholamhossein G. Hamedani

Mathematical and Statistical Science Faculty Research and Publications

In this work, we study the kumaraswamy weibull geometric (Kw-WG) distribution which includes as special cases, several models such as the kumaraswamy weibull distribution, kumaraswamy exponential distribution, weibull geometric distribution, exponential geometric distribution, to name a few. This distribution was monotone and non-monotone hazard rate functions, which are useful in lifetime data analysis and reliability. We derive some basic properties of the Kw-WG distribution including non-central rth-moments, skewness, kurtosis, generating functions, mean deviations, mean residual life, entropy, order statistics and certain characterizations of our distribution. The method of maximum likelihood is used for estimating the model parameters and a simulation …

On Modified Burr Xii-Inverse Exponential Distribution: Prop¬Erties, Characterizations And Applications, Fiaz Ahmad Bhatti, Gholamhossein Hamedani, Haitham M. Yousof, Azeem Ali, Munir Ahmad

Mathematical and Statistical Science Faculty Research and Publications

In this paper, a flexible lifetime distribution with increasing, increasing and decreasing and modified bathtub hazard rate called Modified Burr XII-Inverse Exponential (MBXII-IE) is introduced. The density function of MBXII-IE has exponential, left-skewed, right-skewed and symmetrical shapes. Descriptive measures such as moments, moments of order statistics, incomplete moments, inequality measures, residual life function and reliability measures are theoretically established. The MBXII-IE distribution is characterized via different techniques. Parameters of MBXII-IE distribution are estimated using maximum likelihood method. The simulation study is performed to illustrate the performance of the Maximum Likelihood Estimates (MLEs) of the parameters of the MBXII-IE distribution. The …

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; …

Joint Analysis For Multiple Traits, Zhenchuan Wang

Dissertations, Master's Theses and Master's Reports

This dissertation includes three papers with each distributed in one chapter.

In chapter 1, we proposed an Adaptive Weighting Reverse Regression (AWRR) method to test association between multiple traits and rare variants in a genomic region. AWRR is robust to the directions of effects of causal variants and is also robust to the directions of association of traits. Using extensive simulation studies, we compared the performance of AWRR with canonical correlation analysis (CCA), Single-TOW, and the Weighted Sum Reverse Regression (WSRR). Our results showed that, in all of the simulation scenarios, AWRR is consistently more powerful than CCA. In most …

Algorithms For Reconstruction Of Gene Regulatory Networks From High -Throughput Gene Expression Data, Wenping Deng

Dissertations, Master's Theses and Master's Reports

Understanding gene interactions in complex living systems is one of the central tasks in system biology. With the availability of microarray and RNA-Seq technologies, a multitude of gene expression datasets has been generated towards novel biological knowledge discovery through statistical analysis and reconstruction of gene regulatory networks (GRN). Reconstruction of GRNs can reveal the interrelationships among genes and identify the hierarchies of genes and hubs in networks. The new algorithms I developed in this dissertation are specifically focused on the reconstruction of GRNs with increased accuracy from microarray and RNA-Seq high-throughput gene expression data sets.

The first algorithm (Chapter 2) …

An Efficient Method For Online Identification Of Steady State For Multivariate System, Honglun None Xu

Open Access Theses & Dissertations

Most of the existing steady state detection approaches are designed for univariate signals. For multivariate signals, the univariate approach is often applied to each process variable and the system is claimed to be steady once all signals are steady, which is computationally inefficient and also not accurate. The article proposes an efficient online method for multivariate steady state detection. It estimates the covariance matrices using two different approaches, namely, the mean-squared-deviation and mean-squared-successive-difference. To avoid the usage of a moving window, the process means and the two covariance matrices are calculated recursively through exponentially weighted moving average. A likelihood ratio …

Comparing Various Machine Learning Statistical Methods Using Variable Differentials To Predict College Basketball, Nicholas Bennett

Williams Honors College, Honors Research Projects

The purpose of this Senior Honors Project is to research, study, and demonstrate newfound knowledge of various machine learning statistical techniques that are not covered in the University of Akron’s statistics major curriculum. This report will be an overview of three machine-learning methods that were used to predict NCAA Basketball results, specifically, the March Madness tournament. The variables used for these methods, models, and tests will include numerous variables kept throughout the season for each team, along with a couple variables that are used by the selection committee when tournament teams are being picked. The end goal is to find …

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. …

Impact Of Highway Work Zones On Traffic Crashes: A Case Study In Michigan, Qadri Hafez Shaheen

Master's Theses and Doctoral Dissertations

Infrastructure in the US is severely aged and outdated. This presents a seemingly paradoxical problem in the field of construction management: In order to fix and make roads and highways more safe, construction zones must become inherently less safe in the process. There is a high cost to taxpayers and drivers, as work zones experience a significant amount of crashes and fatalities each year. To mitigate some of the factors that contribute to these crashes, this paper attempts to deliver guidelines on how to update relevant crash data, identify relevant factors, and create recommendations accordingly. The research focused particularly on …

Forensic Detection For Earnings Management In Selected Code Law Nations Of Europe, Jef Lee Garner

Walden Dissertations and Doctoral Studies

This study investigated earnings management in European firms. The private investors became victims of manipulated earnings where few laws offered regulatory oversight. The study forensically examined the attributes of earnings management identified using a discretionary accrual model published in Jones' work and Schippers' work. The firms' managers should fulfil agency theory when they made reporting decisions, and they should act in the investors' best interests to fulfil stewardship theory. The managers failed as they seemed to favor insiders when they reported manipulated earnings to outsiders like small investors even though the managers published financial reports conforming to the International Financial …

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 …

The Importance And Development Of Catastrophe Models, Kevin Schwall

Williams Honors College, Honors Research Projects

The Importance and Development of Catastrophe Models

I thought this was a very interesting project to work on. I was intrigued by catastrophe models and how the insurance industry will be able to use them to better predict natural disasters moving forward. From my research, I found that these models are quite effective, and will only improve as time goes on. As more data is gathered and input into the models, the quality of output will only improve, helping insurers and all of us, in the form of more accurate insurance rates. I was surprised to see just how devastating …

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 …

A Comparison Of Discriminant Function Analysis And Logistic Regression By Categorizing The Incarcerated Mentally Ill, Mona King

Wayne State University Dissertations

Both discriminant function analysis (DFA) and logistic regression (LR) are used to classify subjects into a category/group based upon several explanatory variables (Liong & Foo, 2013). Although the two procedures are generally related, there is no clear advice in the statistical literature on when to use DFA vs. LR, although LR appears to be preferred due to the claim that its underlying assumptions are more easily met (Liong & Foo, 2013). Although DFA and LR use different methods to accomplish their objectives, they can answer the same research questions (Antonogeorgos et al., 2009). This facilitates a practical comparison of their …

Facilitators And Outcomes Of Stem-Education Groups Working Toward Disciplinary Integration, Anna E. Bargagliotti

Mathematics, Statistics and Data Science Faculty Works

There is a growing societal recognition of the need for transdisciplinary scholarly collaboration which can enhance undergraduate physics, science, and engineering education. A regional conference/network with 100 university education researchers in physics and other STEM fields was formed to address three themes (problemsolving, computational thinking, and equity) with multiple goals including to strive for transdisciplinary publications. As part of an ongoing participant observation study, phone interviews were conducted 3-4 months later. One year later, publications that were completed as a result of the conference were analyzed for their disciplinary integration. The papers showed evidence of interdispliciplanry collaboration but transdiciplinary collaboration …

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 …

Catastrophe Modeling With Financial Applications, Jeremy Gensel

Williams Honors College, Honors Research Projects

Catastrophe modeling is used to prepare for losses caused by natural catastrophes such as earthquakes, hurricanes, or tornadoes and man-made catastrophes such as terrorism. Modeled data can be used to create a comprehensive distribution of possible disasters. The distribution gives probabilities of potential catastrophes of different severities occurring over a certain time frame. Calculating potential losses and probability of those losses occurring allows insurance companies to plan and reserve enough money to protect themselves from catastrophic events. Using a catastrophe case study posted online from the Casualty Actuarial Society and R software, this paper shows the use of statistical techniques …

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. …

Goodness Of Fit Via Residual Plots In Item Response Theory, Bryonna Bowen

Theses and Dissertations

Goodness-of-fit criteria developed for the evaluation of item response functions have been examined by many scholars using different theories and criteria. A number of potential graphical analysis approaches, such as residual plots, have been described in literature, but have received little attention from researchers. While many tests of goodness-of-fit are available, those that incorporate the analysis of residuals may be most useful. The unmistakable presence of a pattern in the residual plot for the logistic model item response functions even when we know the model fits raises a red flag up and calls for greater analysis. This study explores different …

Adjusting For Mis-Reporting In Count Data, Gelareh Rahimighazikalayeh

Theses and Dissertations

Any counting system is prone to recording errors including underreporting and overreporting. Ignoring the misreporting pattern in count data can give rise to bias in the estimation of model parameters. Accordingly, Poisson, negative binomial and generalized Poisson regression have been expanded in some instances to capture reporting biases. However, to our knowledge, no program has been developed to allow users to apply all of these models when needed. In the first part of the dissertation, we review the available models for underreported counts and develop a Stata command to estimate Poisson, negative binomial and generalized Poisson regression models for underreported …

Mlb Rule Iv Draft: Valuing Draft Pick Slots, Anthony Cacchione

Dissertations and Theses

This study explored the Net Present Value (NPV) in dollar terms of draft pick slots in the Major League Rule IV Draft. In order to accomplish this, the cumulative performance of players selected in each slot within the draft was evaluated and brought to the Present Value of the time they were selected using a discount rate. The performance of the players was determined using the baseball-reference Wins Above Replacement (WAR) metric. It is intuitive that earlier draft picks are the most valuable; however, it is unclear how quickly the value of draft picks decline. This research demonstrates that the …

Particle Filters For State Estimation Of Confined Aquifers, Graeme Field

UNF Graduate Theses and Dissertations

Mathematical models are used in engineering and the sciences to estimate properties of systems of interest, increasing our understanding of the surrounding world and driving technological innovation. Unfortunately, as the systems of interest grow in complexity, so to do the models necessary to accurately describe them. Analytic solutions for problems with such models are provably intractable, motivating the use of approximate yet still accurate estimation techniques. Particle filtering methods have emerged as a popular tool in the presence of such models, spreading from its origins in signal processing to a diverse set of fields throughout engineering and the sciences including …