Statistics and Probability Commons^™

Score Test And Likelihood Ratio Test For Zero-Inflated Binomial Distribution And Geometric Distribution, Xiaogang Dai

Masters Theses & Specialist Projects

The main purpose of this thesis is to compare the performance of the score test and the likelihood ratio test by computing type I errors and type II errors when the tests are applied to the geometric distribution and inflated binomial distribution. We first derive test statistics of the score test and the likelihood ratio test for both distributions. We then use the software package R to perform a simulation to study the behavior of the two tests. We derive the R codes to calculate the two types of error for each distribution. We create lots of samples to approximate …

A Convolutional Neural Network Model For Species Classification Of Camera Trap Images, Annie Casey

Mathematics Undergraduate Theses

The overall purpose of this study was to automate the manual process of tagging species found in camera trap images using machine learning. The basic design of this study was to implement a Convolutional Neural Network model in Python using the Keras and Tensorflow modules that learn to recognize patterns in images in order to classify what species is in a given image and to label it accordingly. Results of the analysis highlight the importance of a large sample size, the degree of accuracy according to various arguments in the model, effectiveness of multiple layers that include Max Pooling, and …

Physical Activity Across The Lifespan And Liver Cancer Incidence In The Nih-Aarp Diet And Health Study Cohort., Hannah Arem, Erikka Loftfield, Pedro F Saint-Maurice, Neal D Freedman, Charles E Matthews

Epidemiology Faculty Publications

While liver cancer rates in the United States are increasing, 5-year survival is only 17.6%, underscoring the importance of prevention. Physical activity has been associated with lower risk of developing liver cancer, but most studies assess physical activity only at a single point in time, often in midlife. We utilized physical activity data from 296,661 men and women in the NIH-AARP Diet and Health Study cohort to test whether physical activity patterns over the life course could elucidate the importance of timing of physical activity on liver cancer risk. We used group modeling of longitudinal data to create physical activity …

Statistical Properties Of Population Stability Index, Bilal Yurdakul

Dissertations

Population stability is an important concept in model management. It is crucial to monitor whether the current population has changed from the population used during development of a model. For example, has the distribution of credit scores changed, and is the existing credit score model still valid? Population change may occur for many reasons–change in the economic environment, strategic change in the business, policy changes within the company, or changes in regulatory environment.

The population stability index (PSI) is a statistic that measures how much a variable has shifted over time, and is used to monitor applicability of a statistical …

Factors Affecting The Number And Type Of Student Research Products For Chemistry And Physics Students At Primarily Undergraduate Institutions: A Case Study., Birgit Mellis, Patricia Soto, Chrystal D. Bruce, Graciela Lacueva, Anne Wilson, Rasitha Jayasekare

2018 Faculty Bibliography

For undergraduate students, involvement in authentic research represents scholarship that is consistent with disciplinary quality standards and provides an integrative learning experience. In conjunction with performing research, the communication of the results via presentations or publications is a measure of the level of scientific engagement. The empirical study presented here uses generalized linear mixed models with hierarchical bootstrapping to examine the factors that impact the means of dissemination of undergraduate research results. Focusing on the research experiences in physics and chemistry of undergraduates at four Primarily Undergraduate Institutions (PUIs) from 2004–2013, statistical analysis indicates that the gender of the student …

Developing Statistical Methods For Data From Platforms Measuring Gene Expression, Gaoxiang Jia

Statistical Science Theses and Dissertations

This research contains two topics: (1) PBNPA: a permutation-based non-parametric analysis of CRISPR screen data; (2) RCRnorm: an integrated system of random-coefficient hierarchical regression models for normalizing NanoString nCounter data from FFPE samples.

Clustered regularly-interspaced short palindromic repeats (CRISPR) screens are usually implemented in cultured cells to identify genes with critical functions. Although several methods have been developed or adapted to analyze CRISPR screening data, no single spe- cific algorithm has gained popularity. Thus, rigorous procedures are needed to overcome the shortcomings of existing algorithms. We developed a Permutation-Based Non-Parametric Analysis (PBNPA) algorithm, which computes p-values at the gene level …

Introduction To Statistics (Ga Southern), Scott Kersey, Stephen Carden

Mathematics Grants Collections

This Grants Collection for Introduction to Statistics was created under a Round Eight ALG Textbook Transformation Grant.

Affordable Learning Georgia Grants Collections are intended to provide faculty with the frameworks to quickly implement or revise the same materials as a Textbook Transformation Grants team, along with the aims and lessons learned from project teams during the implementation process.

Documents are in .pdf format, with a separate .docx (Word) version available for download. Each collection contains the following materials:

• Linked Syllabus
• Initial Proposal
• Final Report

An Event- And Network-Level Analysis Of College Students’ Maximum Drinking Day, Matthew K. Meisel, Angelo M. Dibello, Sara G. Balestrieri, Miles Q. Ott, Graham T. Diguiseppi, Melissa A. Clark, Nancy P. Barnett

Statistical and Data Sciences: Faculty Publications

Background—Heavy episodic drinking is common among college students and remains a serious public health issue. Previous event-level research among college students has examined behaviors and individual-level characteristics that drive consumption and related consequences but often ignores the social network of people with whom these heavy drinking episodes occur. The main aim of the current study was to investigate the network of social connections between drinkers on their heaviest drinking occasions.

Methods—Sociocentric network methods were used to collect information from individuals in the first-year class (N=1342) at one university. Past-month drinkers (N=972) reported on the characteristics of their heaviest drinking occasion …

Direct Error Driven Learning For Deep Neural Networks With Applications To Bigdata, R. Krishnan, Jagannathan Sarangapani, V. A. Samaranayake

Electrical and Computer Engineering Faculty Research & Creative Works

In this paper, generalization error for traditional learning regimes-based classification is demonstrated to increase in the presence of bigdata challenges such as noise and heterogeneity. To reduce this error while mitigating vanishing gradients, a deep neural network (NN)-based framework with a direct error-driven learning scheme is proposed. To reduce the impact of heterogeneity, an overall cost comprised of the learning error and approximate generalization error is defined where two NNs are utilized to estimate the costs respectively. To mitigate the issue of vanishing gradients, a direct error-driven learning regime is proposed where the error is directly utilized for learning. It …

Statistical Models For Correlated Data, Xiaomeng Niu

Dissertations

Correlated data arise frequently in many studies where multiple response variables or repeatedly measured responses within subjects are correlated. My dissertation topic lies broadly in developing various statistical methodologies for correlated types of data such as longitudinal data, clustered data, and multivariate data.

Multiple response variables might be relevant within subjects. A univariate procedure fitting each response separately does not take into account the correlation among responses. To improve estimation efficiency for the regression parameter, this study proposes two estimation procedures by accommodating correlations among the response variables. The proposed procedures do not require knowledge of the true correlation structure …

Denoising Large Neuroimage Mri Data Using Spatial Random Effect Models, Leonard Chukuma Johnson

Dissertations

Spatial smoothing in Magnetic Resonance image (MRI) involves applying a filter to remove high frequency information and consequently improves signal-to-noise ratio that can greatly aid neurosurgeons in pre-surgical planning stages of tumor resection. This immensely reduces the time spent on Electrical stimulation mapping (ESM) prior to surgery. MRI's three-dimensional data provides voxel intensities with complex spatial relationship. The standard de facto spatial smoothing method, Gaussian Kernel smoothing, is satisfactory since a uniform smoothing is done for the whole brain. Secondly, the kernel smoothing technique assumes normality for the voxel intensity, but there is ample evidence in current research that indicates …

A Study Of Flight Simulation Training Time, Aircraft Training Time, And Pilot Competence As Measured By The Naval Standard Score, Aaron D. Judy

Doctor of Education (Ed.D)

The purpose of the study was to investigate the relationships between US Navy T-45C flight simulation training time, actual aircraft training time, and intermediate and advanced jet pilot competence as measured by the Naval Standard Score (NSS). Examining the relationships between US Navy T-45C flight simulation time and actual aircraft flight time may provide further information on flight simulation training versus actual aircraft training to aviation authorities, flight instructors, the military aviation community, the commercial aviation community, and academia. The study was non-experimental, correlational, causal-comparative with an emphasis upon the establishment of mathematic and predictive relationships using archival data from …

Understanding Natural Keyboard Typing Using Convolutional Neural Networks On Mobile Sensor Data, Travis Siems

Computer Science and Engineering Theses and Dissertations

Mobile phones and other devices with embedded sensors are becoming increasingly ubiquitous. Audio and motion sensor data may be able to detect information that we did not think possible. Some researchers have created models that can predict computer keyboard typing from a nearby mobile device; however, certain limitations to their experiment setup and methods compelled us to be skeptical of the models’ realistic prediction capability. We investigate the possibility of understanding natural keyboard typing from mobile phones by performing a well-designed data collection experiment that encourages natural typing and interactions. This data collection helps capture realistic vulnerabilities of the security …

Updated Guidelines, Updated Curriculum: The Gaise College Report And Introductory Statistics For The Modern Student, Beverly Wood, Megan Mocko, Michelle Everson, Nicholas J. Horton, Paul Velleman

Publications

Since the 2005 American Statistical Association's (ASA) endorsement of the Guidelines for Assessment and Instruction in Statistics Education (GAISE) College Report, changes in the statistics field and statistics education have had a major impact on the teaching and learning of statistics. We now live in a world where "Statistics - the science of learning from data - is the fastest-growing science, technology, engineering, and math (STEM) undergraduate degree in the United States," according to the ASA, and where many jobs demand an understanding of how to explore and make sense of data. In light of these new reports and other …

A Comparison Of Machine Learning Algorithms For Prediction Of Past Due Service In Commercial Credit, Liyuan Liu M.A, M.S., Jennifer Lewis Priestley Ph.D.

Published and Grey Literature from PhD Candidates

Credit risk modeling has carried a variety of research interest in previous literature, and recent studies have shown that machine learning methods achieved better performance than conventional statistical ones. This study applies decision tree which is a robust advanced credit risk model to predict the commercial non-financial past-due problem with better critical power and accuracy. In addition, we examine the performance with logistic regression analysis, decision trees, and neural networks. The experimenting results confirm that decision trees improve upon other methods. Also, we find some interesting factors that impact the commercials’ non-financial past-due payment.

College Students’ Personality Traits In Relation To Career Readiness, Shelby R. Overacker, Carly E. Kalis, Francesca Coppola

Student Publications

This study examined sixty-one Gettysburg College juniors and seniors (31 males, 30 females) to measure how the Big Five personality traits, and whether a student has Type D characteristics, determines if a student is career ready. We collected data through an in-person survey, with questions about personality traits, ambition, career readiness, and demographics. Regression was used to statistically analyze our first hypothesis. The results found that there is a significant positive association between conscientiousness and career readiness, but there is no significant association between extraversion and career readiness. For the second hypothesis, a mediation model was used. We found that …

Perceptions Of Transactional And Transformational Leaders According To Gender, Quinn I. Igram, Andrew N. Garstka, Lindsay D. Harris

Student Publications

The lack of females occupying leadership positions in the modern workplace has prompted the research of this study. In order to better understand the perceptions that exist regarding successful leadership, this study was conducted with the intention of understanding individual leadership style through the Multifactor Leadership Questionnaire, which measures transactional and transformational leadership styles (Bass and Avolio, 1993). 64 male and female participants, made up of 36 students and 28 individuals in the workforce ages 18-61 with an average age of 31 answered 21 questions to assess their leadership style and 1 to measure who they perceived as a successful …

Impact Of Home Field Advantage: Analyzed Across Three Professional Sports, Michael S. Risser, Blake R. Gray, Ryan A. Kelly

Student Publications

We examined the impact of home-field advantage in the NFL, NBA, and MLB. We defined home-field advantage as winning more than 50% of the home games. Additionally, we took into consideration how season length could act as a moderator and influence the impact of home-field advantage. We collected data from the 2015 NBA and MLB seasons and the 2015 and 2016 NFL seasons to determine statistical significance. In total, we got data from 4,141 games to analyze. We found that there is statistical significance that the home team has a better chance of winning than the away team across the …

A Multi-Step Nonlinear Dimension-Reduction Approach With Applications To Bigdata, R. Krishnan, V. A. Samaranayake, Jagannathan Sarangapani

Mathematics and Statistics Faculty Research & Creative Works

In this paper, a multi-step dimension-reduction approach is proposed for addressing nonlinear relationships within attributes. In this work, the attributes in the data are first organized into groups. In each group, the dimensions are reduced via a parametric mapping that takes into account nonlinear relationships. Mapping parameters are estimated using a low rank singular value decomposition (SVD) of distance covariance. Subsequently, the attributes are reorganized into groups based on the magnitude of their respective singular values. The group-wise organization and the subsequent reduction process is performed for multiple steps until a singular value-based user-defined criterion is satisfied. Simulation analysis is …

Prediction Of Lncrna-Disease Associations Based On Inductive Matrix Completion, Chengqian Lu, Mengyun Yang, Feng Luo, Fang-Xiang Wu, Min Li, Yi Pan, Yaohang Li, Jianxin Wang

Computer Science Faculty Publications

Motivation: Accumulating evidences indicate that long non-coding RNAs (lncRNAs) play pivotal roles in various biological processes. Mutations and dysregulations of lncRNAs are implicated in miscellaneous human diseases. Predicting lncRNA–disease associations is beneficial to disease diagnosis as well as treatment. Although many computational methods have been developed, precisely identifying lncRNA–disease associations, especially for novel lncRNAs, remains challenging.

Results: In this study, we propose a method (named SIMCLDA) for predicting potential lncRNA– disease associations based on inductive matrix completion. We compute Gaussian interaction profile kernel of lncRNAs from known lncRNA–disease interactions and functional similarity of diseases based on disease–gene and gene–gene onotology …