Statistics and Probability Commons^™

Reducing Effects Of Dispersal On The Bias Of 2-Sample Mark-Recapture Estimators Of Stream Fish Abundance, James N. Mcnair, Carl R. Ruetz Iii, Ariana Carlson, Jiyeon Suh

Open Access Publishing Support Funded Articles

The 2-sample mark-recapture method with Chapman’s estimator is often used by inland fishery managers to estimate the reach-scale abundance of stream fish. An important assumption of this method is that no dispersal into or out of the study reach occurs between the two samples. Violations of this assumption are probably common in practice, but their effect on bias (systematic error) of abundance estimates is poorly understood, especially in small populations. Estimation methods permitting dispersal exist but, for logistical reasons, often are infeasible for routine assessments in streams. The purpose of this paper is to extend available results regarding effects of …

Coastal Wetland Dynamics Under Sea-Level Rise And Wetland Restoration In The Northern Gulf Of Mexico Using Bayesian Multilevel Models And A Web Tool, Tyler Hardy

Master's Theses

There is currently a lack of modeling framework to predict how relative sea-level rise (SLR), combined with restoration activities, affects landscapes of coastal wetlands with uncertainties accounted for at the entire northern Gulf of Mexico (NGOM). I developed such a modeling framework – Bayesian multi-level models to study the spatial pattern of wetland loss in the NGOM, driven by relative RSLR, vegetation productivity, tidal range, coastal slope, and wave height – all interacting with river-borne sediment availability, indicated by hydrological regimes. These interactions have not been comprehensively investigated before. I further modified this model to assess the efficacy of restoration …

Design, Development And Construct Validation Of The Children’S Dietary Inflammatory Index, Samira Khan, Micheal D. Wirth, Andrew Ortaglia, Christian R. Alvarado, Nitin Shivappa, Thomas Hurley, James R. Hébert

Faculty Publications

Objective: To design and validate a literature-derived, population-based Children’s Dietary Inflammatory Index (C-DII)TM. Design: The C-DII was developed based on a review of literature through 2010. Dietary data obtained from children in 16 different countries were used to create a reference database for computing C-DII scores based on consumption of macronutrients, vitamins, minerals, and whole foods. Construct validation was performed using quantile regression to assess the association between C-reactive protein (CRP) concentrations and C-DII scores. Data Sources: All data used for construct validation were obtained from children between six and 14 years of age (n = 3300) who participated in …

A Math Research Project Inspired By Twin Motherhood, Tiffany N. Kolba

Journal of Humanistic Mathematics

The phenomenon of twins, triplets, quadruplets, and other higher order multiples has fascinated humans for centuries and has even captured the attention of mathematicians who have sought to model the probabilities of multiple births. However, there has not been extensive research into the phenomenon of polyovulation, which is one of the biological mechanisms that produces multiple births. In this paper, I describe how my own experience becoming a mother to twins led me on a quest to better understand the scientific processes going on inside my own body and motivated me to conduct research on polyovulation frequencies. An overview of …

Pretrial Release And Failure-To-Appear In Mclean County, Il, Jonathan Monsma

Student Research – Stevenson Center

Actuarial risk assessment tools increasingly have been employed in jurisdictions across the U.S. to assist courts in the decision of whether someone charged with a crime should be detained or released prior to their trial. These tools should be continually monitored and researched by independent 3^rd parties to ensure that these powerful tools are being administered properly and used in the most proficient way as to provide socially optimal results. McLean County, Illinois began using the Public Safety Assessment-Court^TM (PSA-Court or simply PSA) risk assessment tool beginning in 2016. This study culls data from the McLean County Jail …

A Distance Based Method For Solving Multi-Objective Optimization Problems, Murshid Kamal, Syed Aqib Jalil, Syed Mohd Muneeb, Irfan Ali

Journal of Modern Applied Statistical Methods

A new model for the weighted method of goal programming is proposed based on minimizing the distances between ideal objectives to feasible objective space. It provides the best compromised solution for Multi Objective Linear Programming Problems (MOLPP). The proposed model tackles MOLPP by solving a series of single objective sub-problems, where the objectives are transformed into constraints. The compromise solution so obtained may be improved by defining priorities in terms of the weight. A criterion is also proposed for deciding the best compromise solution. Applications of the algorithm are discussed for transportation and assignment problems involving multiple and conflicting objectives. …

Goalie Analytics: Statistical Evaluation Of Context-Specific Goalie Performance Measures In The National Hockey League, Marc Naples, Logan Gage, Amy Nussbaum

SMU Data Science Review

In this paper, we attempt to improve upon the classic formulation of save percentage in the NHL by controlling the context of the shots and use alternative measures than save percentage. In particular, we find save percentage to be both a weakly repeatable skill and predictor of future performance, and we seek other goalie performance calculations that are more robust. To do so, we use three primary tests to test intra-season consistency, intra-season predictability, and inter-season consistency, and extend the analysis to disentangle team effects on goalie statistics. We find that there are multiple ways to improve upon classic save …

Fuel Flow Reduction Impact Analysis Of Drag Reducing Film Applied To Aircraft Wings, Damon Resnick, Chris Donlan, Nimish Sakalle, Cody Pinkerman

SMU Data Science Review

In this paper, we present an analysis of flight data in order to determine whether the application of the Edge Aerodynamix Conformal Vortex Generator (CVG), applied to the wings of aircraft, reduces fuel flow during cruising conditions of flight. The CVG is a special treatment and film applied to the wings of an aircraft to protect the wings and reduce the non-laminar flow of air around the wings during flight. It is thought that by reducing the non-laminar flow or vortices around and directly behind the wings that an aircraft will move more smoothly through the air and provide a …

Data Center Application Security: Lateral Movement Detection Of Malware Using Behavioral Models, Harinder Pal Singh Bhasin, Elizabeth Ramsdell, Albert Alva, Rajiv Sreedhar, Medha Bhadkamkar

SMU Data Science Review

Data center security traditionally is implemented at the external network access points, i.e., the perimeter of the data center network, and focuses on preventing malicious software from entering the data center. However, these defenses do not cover all possible entry points for malicious software, and they are not 100% effective at preventing infiltration through the connection points. Therefore, security is required within the data center to detect malicious software activity including its lateral movement within the data center. In this paper, we present a machine learning-based network traffic analysis approach to detect the lateral movement of malicious software within the …

Predictions Generated From A Simulation Engine For Gene Expression Micro-Arrays For Use In Research Laboratories, Gopinath R. Mavankal, John Blevins, Dominique Edwards, Monnie Mcgee, Andrew Hardin

SMU Data Science Review

In this paper we introduce the technical components, the biology and data science involved in the use of microarray technology in biological and clinical research. We discuss how laborious experimental protocols involved in obtaining this data used in laboratories could benefit from using simulations of the data. We discuss the approach used in the simulation engine from [7]. We use this simulation engine to generate a prediction tool in Power BI, a Microsoft, business intelligence tool for analytics and data visualization [22]. This tool could be used in any laboratory using micro-arrays to improve experimental design by comparing how predicted …

Data Scientist’S Analysis Toolbox: Comparison Of Python, R, And Sas Performance, Jim Brittain, Mariana Cendon, Jennifer Nizzi, John Pleis

SMU Data Science Review

A quantitative analysis will be performed on experiments utilizing three different tools used for Data Science. The analysis will include replication of analysis along with comparisons of code length, output, and results. Qualitative data will supplement the quantitative findings. The conclusion will provide data support guidance on the correct tool to use for common situations in the field of Data Science.

Predicting Game Day Outcomes In National Football League Games, Josh Klein, Anna Frowein, Chris Irwin

SMU Data Science Review

In this paper, we present a model for predicting the game day outcomes of National Football League games. 3 of the most popular sources for game day predictions are analyzed for comparison. Player data and outcomes from previous games are used, but we also incorporate several weather factors into our models. Over 1,700 games were incorporated and 3 separate models are created using simple regression, principal component analysis, and a recursive model. We also discuss the ethicality of using data science techniques by individuals with the knowledge in order to gain an advantage over a population lacking this specialized training.

Estimation Of Finite Population Mean By Using Minimum And Maximum Values In Stratified Random Sampling, Umer Daraz, Javid Shabbir, Hina Khan

Journal of Modern Applied Statistical Methods

In this paper we have suggested an improved class of ratio type estimators in estimating the finite population mean when information on minimum and maximum values of the auxiliary variable is known. The properties of the suggested class of estimators in terms of bias and mean square error are obtained up to first order of approximation. Two data sets are used for efficiency comparisons.

Cancerin: A Computational Pipeline To Infer Cancer-Associated Cerna Interaction Networks, Duc Do, Serdar Bozdag

Mathematics, Statistics and Computer Science Faculty Research and Publications

MicroRNAs (miRNAs) inhibit expression of target genes by binding to their RNA transcripts. It has been recently shown that RNA transcripts targeted by the same miRNA could “compete” for the miRNA molecules and thereby indirectly regulate each other. Experimental evidence has suggested that the aberration of such miRNA-mediated interaction between RNAs—called competing endogenous RNA (ceRNA) interaction—can play important roles in tumorigenesis. Given the difficulty of deciphering context-specific miRNA binding, and the existence of various gene regulatory factors such as DNA methylation and copy number alteration, inferring context-specific ceRNA interactions accurately is a computationally challenging task. Here we propose a computational …

A Bayesian Beta-Mixture Model For Nonparametric Irt (Bbm-Irt), Ethan A. Arenson, George Karabatsos

Journal of Modern Applied Statistical Methods

Item response models typically assume that the item characteristic (step) curves follow a logistic or normal cumulative distribution function, which are strictly monotone functions of person test ability. Such assumptions can be overly-restrictive for real item response data. A simple and more flexible Bayesian nonparametric IRT model for dichotomous items is introduced, which constructs monotone item characteristic (step) curves by a finite mixture of beta distributions, which can support the entire space of monotone curves to any desired degree of accuracy. An adaptive random-walk Metropolis-Hastings algorithm is proposed to estimate the posterior distribution of the model parameters. The Bayesian IRT …

Mixed Logical And Probabilistic Reasoning In The Game Of Clue, Todd W. Neller, Ziqian Luo

Computer Science Faculty Publications

Neller and Ziqian Luo ’18 presented a means of mixed logical and probabilistic reasoning with knowledge in the popular deductive mystery game Clue. Using at-least constraints, we more efficiently represented and reasoned about cardinality constraints on Clue card deal knowledge, and then employed a WalkSAT-based solution sampling algorithm with a tabu search metaheuristic in order to estimate the probabilities of unknown card places.

Calculus Of The Impossible: Review Of The Improbability Principle (2014) By David Hand And The Logic Of Miracles (2018) By Lásló Mérő, Samuel L. Tunstall

Numeracy

David J. Hand. 2014. The Improbability Principle: Why Coincidences, Miracles, and Rare Events Happen Every Day (New York, NY: Scientific American/Farrar, Straus and Giroux) 288 pp. ISBN: 978-0374175344.

Lásló Mérő. 2018. The Logic of Miracles: Making Sense of Rare, Really Rare, and Impossibly Rare Events (New Haven, CT: Yale University Press) 288 pp. ISBN: 978-0300224153.

David Hand and Lásló Mérő both grapple with the occurrence of seemingly impossible events in these two popular science books. In this comparative review, I describe the two books, and explain why I prefer Hand's treatment of the impossible.

Human Activity Recognition Based On Transfer Learning, Jinyong Pang

USF Tampa Graduate Theses and Dissertations

Human activity recognition (HAR) based on time series data is the problem of classifying various patterns. Its widely applications in health care owns huge commercial benefit. With the increasing spread of smart devices, people have strong desires of customizing services or product adaptive to their features. Deep learning models could handle HAR tasks with a satisfied result. However, training a deep learning model has to consume lots of time and computation resource. Consequently, developing a HAR system effectively becomes a challenging task. In this study, we develop a solid HAR system using Convolutional Neural Network based on transfer learning, which …

Application Of The Fusion Model For Cognitive Diagnostic Assessment With Non-Diagnostic Algebra-Geometry Readiness Test Data, Robert H. Fay

USF Tampa Graduate Theses and Dissertations

This study retrofitted a Diagnostic Classification Model (DCM) known as the Fusion model onto non-diagnostic test data from of the University of Chicago School Mathematics Project (UCSMP) Algebra and Geometry Readiness test post-test used with Transition Mathematics (Third Edition, Field-Trial Version). The test contained 24 multiple-choice middle school math items, and was originally given to 95 advanced 6th grade and 293 7th grade students. The use of these test answers for this study was an attempt to show that by using cognitive diagnostic analysis techniques on test items not constructed for that purpose, highly predictable multidimensional cognitive attribute profiles for …

Machine Learning Methods For Network Intrusion Detection And Intrusion Prevention Systems, Zheni Svetoslavova Stefanova

USF Tampa Graduate Theses and Dissertations

Given the continuing advancement of networking applications and our increased dependence upon software-based systems, there is a pressing need to develop improved security techniques for defending modern information technology (IT) systems from malicious cyber-attacks. Indeed, anyone can be impacted by such activities, including individuals, corporations, and governments. Furthermore, the sustained expansion of the network user base and its associated set of applications is also introducing additional vulnerabilities which can lead to criminal breaches and loss of critical data. As a result, the broader cybersecurity problem area has emerged as a significant concern, with many solution strategies being proposed for both …

Robust Estimation And Inference On Current Status Data With Applications To Phase Iv Cancer Trial, Deo Kumar Srivastava, Liang Zhu, Melissa M. Hudson, Jianmin Pan, Shesh N. Rai

Journal of Modern Applied Statistical Methods

The use of piecewise exponential distributions was proposed by Rai et al. (2013) for analyzing cardiotoxicity data. Some parametric models are proposed, but the focus is on the Weibull distribution, which overcomes the limitation of piecewise exponential.

Predicting River Stage Using Recurrent Neural Networks, Eric Rohli

LSU Master's Theses

River stage prediction is an important problem in the water transportation industry. Accurate river stage predictions provide crucial information to barge and tow boat operators, port terminal captains, and lock management officials. Shallow river levels caused by prolonged drought impact the loading capacity of barges and tow boats. High river levels caused by excessive rainfall or snowmelt allow for greater tow capacities but make downstream transportation and lock management risky. Current academic river height prediction systems utilize either time series statistical analysis or machine learning algorithms to forecast future river heights, but systems that combine these two areas often limit …

Investigations Of Biotremors In The Veiled Chameleon (Chamaeleo Calyptratus), Kathryn C. Laslie

Masters Theses & Specialist Projects

While substrate-borne vibrations are utilized by different reptile species, true conspecific communication via biotremors has not yet been demonstrated in reptiles. This study follows a preliminary report that the veiled chameleon (Chamaeleo calyptratus) could produce biotremors in communicative contexts. I tested chameleon behavioral sensitivity to vibrations by placing them on a dowel attached to a shaker emitting vibrations of 25, 50, 150, 300, and 600 Hz and then measured their changes in velocity before and after the stimulus. I then paired chameleons in various social contexts [anthropogenic disturbance (human disruption of animal); dominance (malemale; female-female C. calyptratus); courtship (male-female C. …

Association Tests For Genetic Effect And Its Interaction With Environmental Factors, Zhengyang Zhou

Statistical Science Theses and Dissertations

My research is in the area of statistical genetics, and it contains three projects: (1) Differentiating the Cochran-Armitage (CA) trend test and Pearson’s chi-square test: location and dispersion; (2) Decomposing Pearson’s chi-square test: a linear regression and its departure from linearity; (3) Testing nonlinear gene-environment (GxE) interaction through varying coefficient and linear mixed models.

(1) In genetic case-control association studies, a standard practice is to perform the CA trend test with 1 degree-of-freedom (df) under the assumption of an additive model. However, when the true genetic model is recessive or near recessive, it is outperformed by Pearson’s chi-square test with …

Mathematical Models, Patty Wagner, Marnie Phipps

Mathematics Grants Collections

This Grants Collection for Mathematical Models was created under a Round Nine 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

Development Of A Tool To Assess Students’ Conceptual Understanding In Introductory Statistics, Nathan L. Tintle, Jill Vander Stoep

Faculty Work Comprehensive List

Few tools exist to assess students’ conceptual understanding in post-secondary, introductory statistics courses. The CAOS test is widely considered to be the gold standard, but was first published in 2007 and does not necessarily reflect some of the changes in student learning at the secondary level. Furthermore, it may not be sensitive enough to measure student conceptual understanding in modern post-secondary statistics courses (e.g., simulation-based inference). In this paper we will describe the process of developing a new instrument which uses some CAOS items, as well as additional new items to improve validity and reliability. We will share the validity …

Reality Versus Grant Application Research “Plans”, Linda Burhansstipanov, Linda U. Krebs, Daniel Petereit, Mark Dignan, Sheikh Iqbal Ahamed, Michele Sargent, Krisin Cina, Kimberly Crawford, Doris Thibeault, Simone Bordeaux, Shalini Kanekar, Golam Mushih Tanimul Ahsan, Drew Williams, Ivor D. Addo

Mathematics, Statistics and Computer Science Faculty Research and Publications

This article describes the implementation of the American Indian mHealth Smoking Dependence Study focusing on the differences between what was written in the grant application compared to what happened in reality. The study was designed to evaluate a multicomponent intervention involving 256 participants randomly assigned to one of 15 groups. Participants received either a minimal or an intense level of four intervention components: (1) nicotine replacement therapy, (2) precessation counseling, (3) cessation counseling, and (4) mHealth text messaging. The project team met via biweekly webinars as well as one to two in-person meetings per year throughout the study. The project …

A Note On Sum, Difference, Product And Ratio Of Kumaraswamy Random Variables, Avishek Mallick, Indranil Ghosh, Gholamhossein G. Hamedani

Mathematics, Statistics and Computer Science Faculty Research and Publications

Explicit expressions for the densities of S = X₁ + X₂ , D = X₁ − X₂ , P = X₁X₂ and R= X₁/X₂ are derived when X₁ and X₂ are independent or sub-independent Kumaraswamy random variables. The expressions appear to involve the incomplete gamma functions. Some possible real life scenarios are mentioned in which such quantities might be of interest.

Hierarchical Bayesian Data Fusion Using Autoencoders, Yevgeniy Vladimirovich Reznichenko

Master's Theses (2009 -)

In this thesis, a novel method for tracker fusion is proposed and evaluated for vision-based tracking. This work combines three distinct popular techniques into a recursive Bayesian estimation algorithm. First, semi supervised learning approaches are used to partition data and to train a deep neural network that is capable of capturing normal visual tracking operation and is able to detect anomalous data. We compare various methods by examining their respective receiver operating conditions (ROC) curves, which represent the trade off between specificity and sensitivity for various detection threshold levels. Next, we incorporate the trained neural networks into an existing data …

Student Gains In Conceptual Understanding In Introductory Statistics With And Without A Curriculum Focused On Simulation-Based Inference, Beth Chance, Stephanie Mendoza, Nathan L. Tintle

Faculty Work Comprehensive List

Using “simulation-based inference” (SBI) such as randomization tests as the primary vehicle for introducing students to the logic and scope of statistical inference has been advocated with the potential of improving student understanding of statistical inference, as well as the statistical investigative process as a whole. Moving beyond the individual class activity, entirely revised introductory statistics curricula centering on these ideas have been developed and tested. In this presentation we will discuss three years of cross-institutional tertiary-level data in the United States comparing SBI-focused curricula and non-SBI curricula (roughly 15,000 students). We examine several pre/post measures of conceptual understanding in …