Statistics and Probability Commons^™

Engaging Students In The Practice Of Statistics Through Undergraduate Research, Debra L. Hydorn

Mathematics Articles

As statisticians, we engage in a variety of activities, some of which are regularly integrated into our undergraduate courses. However, the individual courses that comprise a mathematics or statistics degree program might not provide students with experiences in the broader range of activities that define the practice of statistics. To remedy this situation, faculty can consider developing and mentoring undergraduate research projects. This article briefly discusses the skills that comprise statistical practice along with some course and program options for helping students to develop these skills. Then, types of undergraduate research projects in statistics are described to help faculty generate …

Statistical Methods For Detecting Causal Rare Variants And Analyzing Multiple Phenotypes, Xinlan Yang

Dissertations, Master's Theses and Master's Reports

This dissertation includes two papers with each distributed in one chapter. To date, genome-wide association studies (GWAS) have identified a large number of common variants that are associated with complex diseases successfully. However, the common variants identified by GWAS only account for a small proportion of trait heritability. Many studies showed that rare variants could explain parts of the missing heritability. Since the well-developed common variant detecting methods are underpowered for rare variant association tests unless sample sizes or effect sizes are very large, investigation the roles of rare variants in complex diseases presents substantial challenges. In chapter 1, we …

Statistical Methods For Analyzing Multivariate Phenotypes And Detecting Rare Variant Associations, Huanhuan Zhu

Dissertations, Master's Theses and Master's Reports

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

In chapter 1, I compared the performance of eight multivariate phenotype association tests. The motivation to conduct this power comparison paper is as follows. For nearly 15 years, genome-wide association studies (GWAS) have been widely used to identify genetic variants associated with human diseases and traits. GWAS typically investigate genetic variants for a predefined phenotype, thus fail to identify weak but important effects. In recent years, many multivariate association tests have been developed. However, there is a lack of comprehensive summary of such kinds of approaches. To fill this …

Offline And Online Density Estimation For Large High-Dimensional Data, Aref Majdara

Dissertations, Master's Theses and Master's Reports

Density estimation has wide applications in machine learning and data analysis techniques including clustering, classification, multimodality analysis, bump hunting and anomaly detection. In high-dimensional space, sparsity of data in local neighborhood makes many of parametric and nonparametric density estimation methods mostly inefficient.

This work presents development of computationally efficient algorithms for high-dimensional density estimation, based on Bayesian sequential partitioning (BSP). Copula transform is used to separate the estimation of marginal and joint densities, with the purpose of reducing the computational complexity and estimation error. Using this separation, a parallel implementation of the density estimation algorithm on a 4-core CPU is …

Application Of Remote Sensing And Machine Learning Modeling To Post-Wildfire Debris Flow Risks, Priscilla Addison

Dissertations, Master's Theses and Master's Reports

Historically, post-fire debris flows (DFs) have been mostly more deadly than the fires that preceded them. Fires can transform a location that had no history of DFs to one that is primed for it. Studies have found that the higher the severity of the fire, the higher the probability of DF occurrence. Due to high fatalities associated with these events, several statistical models have been developed for use as emergency decision support tools. These previous models used linear modeling approaches that produced subpar results. Our study therefore investigated the application of nonlinear machine learning modeling as an alternative. Existing 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 …

An Analysis Of Equity-Linked Insurance Pricing, Clara C. Ortgies

Honors Program Theses

This comprehensive study of equity-linked insurance options will explore the pricing of certificates of deposit and life insurance options using a present value method. With this study, I will be able to construct and price various equity-linked insurance products, with a focus on life insurance, that insurance companies could then sell to prospective customers. I will use concepts and formulas based in actuarial math, probability theory, and financial engineering in order to construct, price, and analyze new equity-linked insurance products. The fundamental methodology I will use involves applying pricing theory based on the expected value of the insurance payoff present …

A Land Use Regression Model For Explaining Spatial Variation In Air Pollution Levels Using A Wind Sector Based Approach, Owen Naughton, Aoife Donnelly, Paul Nolan, Francesco Pilla, Bruce Misstear, Brian Broderick

Articles

Estimating pollutant concentrations at a local and regional scale is essential for good ambient air quality information in environmental and health policy decision making. Here we present a land use regression (LUR) modelling methodology that exploits the high temporal resolution of fixed-site monitoring (FSM) to produce viable air quality maps. The methodology partitions concentration time series from a national FSM network into wind-dependent sectors or “wedges”. A LUR model is derived using predictor variables calculated within the directional wind sectors, and compared against the long-term average concentrations within each sector. This study demonstrates the value of incorporating the relative position …

Accumulating Evidence Of The Impact Of Voter Id Laws: Student Engagement In The Political Process, Kelly Mcconville, L. Stokes, M. Gray

Mathematics & Statistics Faculty Works

Recently, voter ID laws have been instituted, modified or overturned in many states in the US. As these laws change, it is important to have accurate measures of their impact. We present the data collection methods and results of class projects that attempted to quantify the impact of the voter ID laws in areas of three states. We also summarize the types of data used to assess the impact of voter ID laws and discuss how our data address some of the shortcomings of the usual techniques for assessing the impact of voter ID laws.

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 Rotatable Asymmetric Variable Compensation Mirt Model, Xinchu Zhao

Theses and Dissertations

The purpose of this study is to develop, estimate, and interpret a new variable compensation multidimensional item response theory (MIRT) model, named the Rotatable Asymmetric Variable Compensation Model (RAVCM), that allows for transformation between different correlation structures. Since the model is rotatable like the common compensatory models (CM), it is not necessary to specify or estimate the correlation of abilities to recover the model. Also, it can approximate the existing MIRT models well. In simulation, the RAVCM is shown to estimate the parameters with small error, especially when the non-compensatory model (NCM) is the true model and the correlation of …

Netnographic Slog: Creative Elicitation Strategies To Encourage Participation In An Online Community Of Practice For Early Education And Care, Ruth Wallace

Research outputs 2014 to 2021

Active, participatory netnography, in contrast to passive netnography, is essential if researchers are to gain rich rewards from the rigorous collection of qualitative data. However, researchers should be aware of the ‘netnographic slog’; “the blood, sweat and tears” associated with eliciting quality data and encouraging active participation in online communities.

This article examines the – Supporting Nutrition for Australian Childcare (SNAC) – online community of practice, established to support healthy eating practices in early childhood education and care settings. To ensure research rigour, Kozinets’ netnographic steps were employed. Garnering member participation in this online community was a slog; most community …

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 …

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 …

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 …