Statistics and Probability Commons^™

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 …

Extended Poisson Models For Count Data With Inflated Frequencies, Monika Arora

Mathematics & Statistics Theses & Dissertations

Count data often exhibits inflated counts for zero. There are numerous papers in the literature that show how to fit Poisson regression models that account for the zero inflation. However, in many situations the frequencies of zero and of some other value k tends to be higher than the Poisson model can fit appropriately. Recently, Sheth-Chandra (2011), Lin and Tsai (2012) introduced a mixture model to account for the inflated frequencies of zero and k. In this dissertation, we study basic properties of this mixture model and parameter estimation for grouped and ungrouped data. Using stochastic representation we show …

Robust Heteroscedasticity Consistent Covariance Matrix Estimator Based On Robust Mahalanobis Distance And Diagnostic Robust Generalized Potential Weighting Methods In Linear Regression, M. Habshah, Muhammad Sani, Jayanthi Arasan

Journal of Modern Applied Statistical Methods

The violation of the assumption of homoscedasticity and the presence of high leverage points (HLPs) are common in the use of regression models. The weighted least squares can provide the solution to heteroscedastic regression model if the heteroscedastic error structures are known. Based on Furno (1996), two robust weighting methods are proposed based on HLP detection measures (robust Mahalanobis distance based on minimum volume ellipsoid and diagnostic robust generalized potential based on index set equality (DRGP(ISE)) on robust heteroscedasticity consistent covariance matrix estimators. Results obtained from a simulation study and real data sets indicated the DRGP(ISE) method is superior.

Masked Instability: Within-Sector Financial Risk In The Presence Of Wealth Inequality, Youngna Choi

Department of Applied Mathematics and Statistics Faculty Scholarship and Creative Works

We investigate masked financial instability caused by wealth inequality. When an economic sector is decomposed into two subsectors that possess a severe wealth inequality, the sector in entirety can look financially stable while the two subsectors possess extreme financially instabilities of opposite nature, one from excessive equity, the other from lack thereof. The unstable subsector can result in further financial distress and even trigger a financial crisis. The market instability indicator, an early warning system derived from dynamical systems applied to agent-based models, is used to analyze the subsectoral financial instabilities. Detailed mathematical analysis is provided to explain what financial …

Internal Consistency Reliability In Measurement: Aggregate And Multilevel Approaches, Georgios Sideridis, Abdullah Saddaawi, Khaleel Al-Harbi

Journal of Modern Applied Statistical Methods

The purpose of the present paper was to evaluate the internal consistency reliability of the General Teacher Test assuming clustered and non-clustered data using commercial software (Mplus). Participants were 2,000 testees who were selected using random sampling from a larger pool of examinees (more than 65k). The measure involved four factors, namely: (a) planning for learning, (b) promoting learning, (c) supporting learning, and (d) professional responsibilities and was hypothesized to comprise a unidimensional instrument assessing generalized skills and competencies. Intra-class correlation coefficients and variance ratio statistics suggested the need to incorporate a clustering variable (i.e., university) when evaluating the factor …

Applying Conditional Distributions To Individuals: Using Latent Variable Models, Feng Ji

Theses and Dissertations

This study proposes a new method to interpret individual results of psychological test batteries. The Mahalanobis distance is a commonly-used measure of how unusual an individual’s profile of scores is compared to a population of score profiles. In models in which there is a set of predictors and a set of dependent variables (e.g., cognitive abilities predicting academic abilities), it is useful to distinguish between a profile of dependent scores that is unusual because its profile of predictor scores is unusual and a profile of dependent scores that is unusual even after controlling for the predictors. The conditional Mahalanobis distance …

Fitting The Rasch Model Under The Logistic Regression Framework To Reduce Estimation Bias, Tianshu Pan

Journal of Modern Applied Statistical Methods

This article showed how and why the Rasch model can be fitted under the logistic regression framework. Then a penalized maximum likelihood (Firth 1993) for logistic regression models can also be used to reduce ML biases when fitting the Rasch model. These conclusions are supported by a simulation study.

Robust Computation In 2d Absolute Eit (A-Eit) Using D-Bar Methods With The “Exp” Approximation, Sarah J. Hamilton, J. L. Mueller, T. R. Santos

Mathematical and Statistical Science Faculty Research and Publications

Objective

Absolute images have important applications in medical Electrical Impedance Tomography (EIT) imaging, but the traditional minimization and statistical based computations are very sensitive to modeling errors and noise. In this paper, it is demonstrated that D-bar reconstruction methods for absolute EIT are robust to such errors.

Approach

The effects of errors in domain shape and electrode placement on absolute images computed with 2-D D-bar reconstruction algorithms are studied on experimental data.

Main Results

It is demonstrated with tank data from several EIT systems that these methods are quite robust to such modeling errors, and furthermore the artefacts arising from …

Regressions Regularized By Correlations, Stan Lipovetsky

Journal of Modern Applied Statistical Methods

The regularization of multiple regression by proportionality to correlations of predictors with dependent variable is applied to the least squares objective and normal equations to relax the exact equalities and to get a robust solution. This technique produces models not prone to multicollinearity and is very useful in practical applications.

An Explanatory Study On The Non-Parametric Multivariate T2 Control Chart, Abdolrasoul Mostajeran, Nasrolah Iranpanah, Rassoul Noorossana

Journal of Modern Applied Statistical Methods

Most control charts require the assumption of normal distribution for observations. When distribution is not normal, one can use non-parametric control charts such as sign control chart. A deficiency of such control charts could be the loss of information due to replacing an observation with its sign or rank. Furthermore, because the chart statistics of T² are correlated, the T² chart is not a desire performance. Non-parametric bootstrap algorithm could help to calculate control chart parameters using the original observations while no assumption regarding the distribution is needed. In this paper, first, a bootstrap multivariate control chart is …

Optimum Stratification In Bivariate Auxiliary Variables Under Neyman Allocation, Faizan Danish, S.E.H. Rizvi

Journal of Modern Applied Statistical Methods

In several situations complete data set of the study variable is unknown that becomes a stumbling block in various stratification techniques in order to obtain stratification points on two way stratification method. In this paper a technique has been proposed under Neyman allocation when the stratification is done oj the two auxiliary variable having one estimation variable under consideration. Due to complexities created by minimal equations approximate optimum strata boundaries has been obtained. Empirical study has been done to illustrate the proposed method when the auxiliary variables have standard Cauchy and power distributions.

Presence Of Antibiotic-Resistant Escherichia Coli In Wastewater Treatment Plant Effluents Utilized As Water Reuse For Irrigation, Asli Aslan, Zach A. Coles, Anunay Bhattacharya, Oghenekpaobor Oyibo

Biostatistics, Epidemiology & Environmental Health Sciences: Faculty Publications

Providing safe water through water reuse is becoming a global necessity. One concern with water reuse is the introduction of unregulated contaminants to the environment that cannot be easily removed by conventional wastewater treatment plants (WWTP). The occurrence of ampicillin, sulfamethoxazole, ciprofloxacin, and tetracycline-resistant Escherichia coli through the treatment stages of a WWTP (raw sewage, post-secondary, post-UV and post-chlorination) was investigated from January to May 2016. The highest concentrations of antibiotic resistant E. coli in the effluent were detected in April after rainfall. Ampicillin-resistant E. coli was the most common at the post UV and chlorination stages comprising 63% of …

Development Of A Slab-Based Monte Carlo Proton Dose Algorithm With A Robust Material-Dependent Nuclear Halo Model, John Wesley Chapman Jr

LSU Doctoral Dissertations

Pencil beam algorithms (PBAs) are often utilized for dose calculation in proton therapy treatment planning because they are fast and accurate under most conditions. However, as discussed in Chapman et al (2017), the accuracy of a PBA can be limited under certain conditions because of two major assumptions: (1) the central-axis semi-infinite slab approximation; and, (2) the lack of material dependence in the nuclear halo model. To address these limitations, we transported individual protons using a class II condensed history Monte Carlo and added a novel energy loss method that scaled the nuclear halo equation in water to arbitrary geometry. …

A Mapping Review Of Randomized Controlled Trials In The Spinal Cord Injury Research Literature, Amanda Mcintyre, Brooke Benton, Shannon Janzen, Jerome Iruthayarajah, Joshua Weiner, Janice J. Eng, Robert Teasell, Spinal Cord Injury Research Evidence Team

Epidemiology and Biostatistics Publications

Study design

Mapping Review.

Objective

The objective of this study was to map out and characterize the quantity and quality of all published spinal cord injury (SCI) randomized controlled trials (RCTs) with respect to number, sample size, and methodological quality between January 1970 and December 2016.

Setting

Not applicable.

Methods

A literature search of multiple research databases was conducted. Studies adhering to the following criteria were included: the research design was an RCT; written in English; participants were >18 years; and the sample was >50% SCI. Data were extracted pertaining to author(s), year of publication, country of origin, initial and …

A Comparison Of The Predictive Ability Of Logistic Regression And Time Series Analysis On Business Credit Data, Lauren Staples

Published and Grey Literature from PhD Candidates

The credit industry creates models to determine the risk of lending money to consumers as well as to commercial customers. These models are heavily regulated in the U.S. as well as in other countries. Model inputs must be explainable to customers as well as to regulators. Two such modeling approaches that are currently commonly used are logistic regression models and time series models. This paper steps through the preprocessing and model building of these two models on a large commercial data set and compares the predictive ability of these two methods. The two models achieved similar accuracy results: the logistic …

Recent Trends In The Frequency And Duration Of Global Floods, Nasser Najibi, Naresh Devineni

Publications and Research

Frequency and duration of floods are analyzed using the global flood database of the Dartmouth Flood Observatory (DFO) to explore evidence of trends during 1985–2015 at global and latitudinal scales. Three classes of flood duration (i.e., short: 1–7, moderate: 8–20, and long: 21 days and above) are also considered for this analysis. The nonparametric Mann–Kendall trend analysis is used to evaluate three hypotheses addressing potential monotonic trends in the frequency of flood, moments of duration, and frequency of specific flood duration types. We also evaluated if trends could be related to large-scale atmospheric teleconnections using a generalized linear model framework. …