Statistics and Probability Commons^™

Disparities In Prostate Cancer Survival In Appalachian Kentucky: A Population-Based Study, Zin W. Myint, Richard O'Neal, Quan Chen, Bin Huang, Robin C. Vanderpool, Peng Wang

Internal Medicine Faculty Publications

Introduction: Prostate cancer (PC) is the most common male cancer in the USA. When comparing the incidence and mortality rates of PC, the Surveillance Epidemiology and End Results data of 2005–2014 show that Appalachian Kentucky had a lower incidence (113/100 000 v 137/100 000) but a higher mortality rate (23.8% v 21.8%) when compared to non-Appalachian Kentucky. The aim of this study was to further characterize the survival disparities of PC between Appalachian and non-Appalachian Kentucky.

Methods: All stages of PC patients diagnosed between 2007 and 2011 were collected through the Kentucky Cancer Registry. Baseline characteristics and survival outcomes were …

Analyzing Two-Year College Student Success Using Structural Equation Modeling, Jessica Taylor

Graduate Theses, Dissertations, and Capstones

The goal of this study is to more fully understand the scope of community college student success using the principles of mindset, engagement, and college readiness. Using structural equation modeling ensures this study is able to measure the combined effects these concepts have on student success, group differences, and the combined model of student success. Findings suggest student success can be significantly impacted by self-belief and mindset behaviors that can outweigh the initial effect of academically under-prepared students. Groups included in this study are non-traditional students, minority populations, first generation students, and Pell eligible students.

Predictive Validity Of A New Self-Report Measure Of Individual Skin Type Through Characterization Of Skin Melanin Using Reflectance Photospectroscopy, Mark Sanchez, Lisa Marriott, Teala Alvord, R. Samatham, S. Chang

Student Research Symposium

In the realm of research and dermatology, the Fitzpatrick Skin Type scale (FST) has been the gold standard of measurement to classify sun sensitivity for human’s skin. This scale is based on an individual’s dermal reaction to ultraviolet exposure (Parrish, et al., 1974; Fitzpatrick, 1975; Pathak, et al., 1976; Fitzpatrick, 1988). It was assumed in science as well as popular culture that individuals with darker skin were less susceptible to issues related to UV damage of their skin. More recent research (Eilers, et al., 2013) suggests that while melanin affords some skin protection, damage can still occur that may result …

Upper Record Values From Extended Exponential Distribution, Devendra Kumar, Sanku Dey

Journal of Modern Applied Statistical Methods

Some recurrence relations are established for the single and product moments of upper record values for the extended exponential distribution by Nadarajah and Haghighi (2011) as an alternative to the gamma, Weibull, and the exponentiated exponential distributions. Recurrence relations for negative moments and quotient moments of upper record values are also obtained. Using relations of single moments and product moments, means, variances, and covariances of upper record values from samples of sizes up to 10 are tabulated for various values of the shape parameter and scale parameter. A characterization of this distribution based on conditional moments of record …

Measurement Of Bystander Actions In Violence Intervention Evaluation: Opportunities And Challenges, Heather M. Bush, Samuel C. Bell, Ann L. Coker

Biostatistics Faculty Publications

Purpose of Review

This review discusses design and methodological challenges specific to measuring bystander actions in the evaluation of bystander-based violence prevention programming. “Bystanders” are defined as people who are present immediately before, during and/or after a violent event, but are not a perpetrator nor the intended victim. Bystander-based violence prevention programs seek to prevent or mitigate violent events by empowering bystanders to intervene on acts of violence and social norms that promulgate violence.

Recent Findings

Effective bystander-based violence prevention programs demonstrate increased bystander intentions, actions, and attitudes [Bringing in the Bystander: Banyard et al. J Community Psychol. 2007;35:463-481; iSCREAM: …

Twitter And Disasters: A Social Resilience Fingerprint, Benjamin A. Rachunok, Jackson B. Bennett, Roshanak Nateghi

Purdue University Libraries Open Access Publishing Fund

Understanding the resilience of a community facing a crisis event is critical to improving its adaptive capacity. Community resilience has been conceptualized as a function of the resilience of components of a community such as ecological, infrastructure, economic, and social systems, etc. In this paper, we introduce the concept of a “resilience fingerprint” and propose a multi-dimensional method for analyzing components of community resilience by leveraging existing definitions of community resilience with data from the social network Twitter. Twitter data from 14 events are analyzed and their resulting resilience fingerprints computed. We compare the fingerprints between events and show that …

Bias Reduction In Machine Learning Classifiers For Spatiotemporal Analysis Of Coral Reefs Using Remote Sensing Images, Justin J. Gapper

Computational and Data Sciences (PhD) Dissertations

This dissertation is an evaluation of the generalization characteristics of machine learning classifiers as applied to the detection of coral reefs using remote sensing images. Three scientific studies have been conducted as part of this research: 1) Evaluation of Spatial Generalization Characteristics of a Robust Classifier as Applied to Coral Reef Habitats in Remote Islands of the Pacific Ocean 2) Coral Reef Change Detection in Remote Pacific Islands using Support Vector Machine Classifiers 3) A Generalized Machine Learning Classifier for Spatiotemporal Analysis of Coral Reefs in the Red Sea. The aim of this dissertation is to propose and evaluate a …

Asl Reverse Dictionary - Asl Translation Using Deep Learning, Ann Nelson, Kj Price, Rosalie Multari

SMU Data Science Review

The challenges of learning a new language can be reduced with real-time feedback on pronunciation and language usage. Today there are readily available technologies which provide such feedback on spoken languages, by translating the voice of the learner into written text. For someone seeking to learn American Sign Language (ASL), there is however no such feedback application available. A learner of American Sign Language might reference websites or books to obtain an image of a hand sign for a word. This process is like looking up a word in a dictionary, and if the person wanted to know if they …

What Makes A Good Research Consultant?, Justin Harding, Samantha Estrada, Michael Floren

The Qualitative Report

Statistical and research consulting is defined as the collaboration of a statistician or methodologist with another professional for devising solutions to research problems. An in-depth, interview qualitative approach was taken to answer the research question of what makes a good research consultant. The authors interviewed four faculty members in the field of statistics and research methods and two experienced graduate student consultants. In-depth, face-to-face interviews revealed common themes regarding consultancy skills, resourcefulness, communication and interpersonal skills. The participants discussed how to improve consulting sessions and deal with clients with different statistics levels and backgrounds. Participants felt there was no difference …

Combining Chicken Retina Rna-Seq Data Across Studies To Strengthen Biomarker Detection, Sarah Szvetecz

Senior Honors Projects, 2010-2019

Various studies have identified the chicken embryo (Gallus gallus) as a useful model to study the retinogenesis process in humans. This project uses data from two specific RNA sequencing (RNA-seq) studies to investigate retina developmental biology. These studies are done in two different labs using different protocols, as such they cannot be compared directly. Study 1 contains chicken retina samples from embryonic day 3, 5 and 8; while study 2 has retina samples from embryonic day 8, 16, and 18 of developmental age. We apply a normalization method on both studies to account for differences in the two …

The Reproducibility Crisis In Scientific Research, Sarah Eline

Senior Honors Projects, 2010-2019

Following the push for evidence based practice, came a huge proliferation of research journals and journal articles. With this increase in quantity came an increased concern about the quality of these articles being published, which led to a multifield investigation regarding the reproducibility of scientific research. With studies in the fields of psychology and biomedicine only reaching approximately a 30% reproducibility rate, a conversation has been sparked that spans across every field of research. Upon further investigation, various causes for this reproducibility crisis have surfaced which include, lack of data sharing/ transparency, statistical errors, funding corruption, and the culture surrounding …

Demand Forecasting: An Open-Source Approach, Murtada Shubbar, Jared Smith

SMU Data Science Review

In this paper, we compare demand forecasting methods used by the supply chain department at Bilports to open-source forecasting methods. The design and implementation of the open-source forecasting system also attempts to use several external datasets such as consumer sentiment, housing permit starts, and weather to improve prediction quality. Additionally, the performance of the forecast is evaluated by the reduction of shipment lead times from China, the company’s primary vendor. The objective of our paper is to improve Bilports’s forecasting capabilities. The primary motivation of this paper is to increase forecasting accuracy and identify the weaknesses of the methods used …

Kadafrica: Survey Analysis To Support Research For Smallholder Farmers, Gregory Asamoah, Robert Gill, Frank Sclafani, Bivin Sadler

SMU Data Science Review

In this paper, we present an analysis of survey data with the goal of determining if the KadAfrica training program, a social organization in Uganda, has a significant effect on the lives of the girls who participate in the program. This is done through an observational study of girl’s responses to several pre-program and post-program questions. These questions include topics such as the girl’s access to hygiene materials and their personal views on family finances. In addition to providing an analysis of historical data, we established a data platform in which future data can be stored and analyzed in an …

Leveraging Reviews To Improve User Experience, Anthony Schams, Iram Bakhtiar, Cristina Stanley

SMU Data Science Review

In this paper, we will explore and present a method of finding characteristics of a restaurant using its reviews through machine learning algorithms. We begin by building models to predict the ratings of individual reviews using text and categorical features. This is to examine the efficacy of the algorithms to the task. Both XGBoost and logistic regression will be examined. With these models, our goal is then to identify key phrases in reviews that are correlated with positive and negative experience. Our analysis makes use of review data publicly made available by Yelp. Key bigrams extracted were non-specific to the …

Repairing Landsat Satellite Imagery Using Deep Machine Learning Techniques, Griffin J. Lane, Patricia Goresen, Robert Slater

SMU Data Science Review

Satellite Imagery is one of the most widely used sources to analyze geographic features and environments in the world. The data gathered from satellites are used to quantify many vital problems facing our society, such as the impact of natural disasters, shore erosion, rising water levels, and urban growth rates. In this paper, we construct machine learning and deep learning algorithms for repairing anomalies in the Landsat satellite imagery data which arise for various reasons ranging from cloud obstruction to satellite malfunctions. The accuracy of GIS data is crucial to ensuring the models produced from such data are as close …

Visualization And Machine Learning Techniques For Nasa’S Em-1 Big Data Problem, Antonio P. Garza Iii, Jose Quinonez, Misael Santana, Nibhrat Lohia

SMU Data Science Review

In this paper, we help NASA solve three Exploration Mission-1 (EM-1) challenges: data storage, computation time, and visualization of complex data. NASA is studying one year of trajectory data to determine available launch opportunities (about 90TBs of data). We improve data storage by introducing a cloud-based solution that provides elasticity and server upgrades. This migration will save $120k in infrastructure costs every four years, and potentially avoid schedule slips. Additionally, it increases computational efficiency by 125%. We further enhance computation via machine learning techniques that use the classic orbital elements to predict valid trajectories. Our machine learning model decreases trajectory …

Machine Learning Pipeline For Exoplanet Classification, George Clayton Sturrock, Brychan Manry, Sohail Rafiqi

SMU Data Science Review

Planet identification has typically been a tasked performed exclusively by teams of astronomers and astrophysicists using methods and tools accessible only to those with years of academic education and training. NASA’s Exoplanet Exploration program has introduced modern satellites capable of capturing a vast array of data regarding celestial objects of interest to assist with researching these objects. The availability of satellite data has opened up the task of planet identification to individuals capable of writing and interpreting machine learning models. In this study, several classification models and datasets are utilized to assign a probability of an observation being an exoplanet. …

Machine Learning Vs Conventional Analysis Techniques For The Earth’S Magnetic Field Study, Sheri Loftin, Sarah J. Fite, Laura V. Bishop, Stavros Kotsiaros

SMU Data Science Review

Abstract. Current techniques for calculating and generating models used for analyzing the Earth’s magnetic field are laborious and time-consuming. We assert that machine learning can have a significant impact on building magnetic field models more quickly and on various levels of complexity, specifically as it pertains to data cleansing and sorting. Our approach to this problem uses a reverse iterative multi-phase process for data cleansing, in which, initially, the CHAOS-6 model data is examined to determine if machine learning can be used to differentiate between useful data components for spherical harmonics, versus data noise. During this phase, six different machine …

Leveraging Natural Language Processing Applications And Microblogging Platform For Increased Transparency In Crisis Areas, Ernesto Carrera-Ruvalcaba, Johnson Ekedum, Austin Hancock, Ben Brock

SMU Data Science Review

Through microblogging applications, such as Twitter, people actively document their lives even in times of natural disasters such as hurricanes and earthquakes. While first responders and crisis-teams are able to help people who call 911, or arrive at a designated shelter, there are vast amounts of information being exchanged online via Twitter that provide real-time, location-based alerts that are going unnoticed. To effectively use this information, the Tweets must be verified for authenticity and categorized to ensure that the proper authorities can be alerted. In this paper, we create a Crisis Message Corpus from geotagged Tweets occurring during 7 hurricanes …

Tidying And Analysis Of The 2014 Texas English Ii End-Of-Course Exam, David Churchman, Abigail Morton Garland

SMU Data Science Review

The state of Texas requires all public high school students to take End of Course (EOC) exams. The results of these exams are made nominally public, but in a shape and format that precludes ready analysis. To the extent possible, principles of tidy data will be applied to clean and analyze the publicly released data file for the 2014 English II EOC exam, providing insights into the EOC program and a case for better public data from the Texas Education Administration (TEA).