Statistics and Probability Commons^™

The Relationship Between Exercise And Depression And Anxiety In College Students, Joshua Frank, Dr. Amy Adkins, Nathan Thomas, Dr. Danielle Dick

UROP Posters

The literature shows an inverse association between exercise and mental disorders. The aim of this study is to further elaborate on this association with regards to exercise and its relationship with anxiety and depression in a college sample. The subject group focused on seniors in the Spit for Science data set which incorporated a total of 821 students. Physical activity was assessed using the International Physical Activity Questionnaire (IPAQ) to estimate the overall metabolic equivalents (MET’s) each student spent in walking, moderate, or vigorous activity levels in the previous week. Sum scores were used to measure depression and anxiety. Overall,the …

An Analysis Of Accuracy Using Logistic Regression And Time Series, Edwin Baidoo, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

This paper analyzes the accuracy rates for logistic regression and time series models. It also examines a relatively new performance index that takes into consideration the business assumptions of credit markets. Although prior research has focused on evaluation metrics, such as AUC and Gini index, this new measure has a more intuitive interpretation for various managers and decision makers and can be applied to both Logistic and Time Series models.

A Comparison Of Machine Learning Techniques And Logistic Regression Method For The Prediction Of Past-Due Amount, Jie Hao, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

The aim of this paper to predict a past-due amount using traditional and machine learning techniques: Logistic Analysis, k-Nearest Neighbor and Random Forest. The dataset to be analyzed is provided by Equifax, which contains 305 categories of financial information from more than 11,787,287 unique businesses from 2006 to 2014. The big challenge is how to handle with the big and noisy real world datasets. Among the three techniques, the results show that Logistic Regression Method is the best in terms of predictive accuracy and type I errors.

Application Of Isotonic Regression In Predicting Business Risk Scores, Linh T. Le, Jennifer L. Priestley

Published and Grey Literature from PhD Candidates

An isotonic regression model fits an isotonic function of the explanatory variables to estimate the expectation of the response variable. In other words, as the function increases, the estimated expectation of the response must be non-decreasing. With this characteristic, isotonic regression could be a suitable option to analyze and predict business risk scores. A current challenge of isotonic regression is the decrease of performance when the model is fitted in a large data set e.g. more than four or five dimensions. This paper attempts to apply isotonic regression models into prediction of business risk scores using a large data set …

A Saddlepoint Approximation To Left-Tailed Hypothesis Tests Of Variance For Non-Normal Populations, Tyler L. Grimes

UNF Graduate Theses and Dissertations

When the variance of a single population needs to be assessed, the well-known chi-squared test of variance is often used but relies heavily on its normality assumption. For non-normal populations, few alternative tests have been developed to conduct left tailed hypothesis tests of variance. This thesis outlines a method for generating new test statistics using a saddlepoint approximation. Several novel test statistics are proposed. The type-I error rates and power of each test are evaluated using a Monte Carlo simulation study. One of the proposed test statistics, R_gamma2, controls type-I error rates better than existing tests, while having comparable power. …

The Association Between Osteoporosis And Early Menopause Following Hysterectomy, Mia Meeyaong-Won Botkin

Walden Dissertations and Doctoral Studies

Osteoporosis is considered to be the most adverse public health disease associated with substantial mortality among postmenopausal women. Hysterectomy, surgically induced menopause, contributes to the early onset of menopause. However, there was no evidence of an association between early menopause following hysterectomy and osteoporosis among postmenopausal women. The purpose of this quantitative study was to examine the association between demographic and behavioral factors and the prevalence of osteoporosis among hysterectomized postmenopausal women. The integrated theory of health behavior change theoretical framework guided study. Cross-sectional secondary data from the 2009-2010 National Health and Nutrition Examination Survey were used. Multiple logistic regression …

Modern Estimation Problems In Group Testing, Md Shamim Sarker

Theses and Dissertations

In the simplest form of group testing, pools are formed by compositing a fixed number of individual specimens (e.g., blood, urine, swab, etc.) and then the pools are tested for a binary characteristic, such as presence or absence of a disease. Group testing is commonly used to screen for a variety of sexually transmitted diseases in epidemiological applications where the main goal is to increase testing efficiency. In this dissertation, we study three estimation problems that are motivated by real-life applications. We propose new methods to model group testing data for both single and multiple infections. In the first problem, …

Semiparametric Joint Dynamic Modeling Of A Longitudinal Marker, Recurrent Competing Risks, And A Terminal Event, Piaomu Liu

Theses and Dissertations

The joint modeling framework has found extensive applications in cancer and other biomedical research. For example, recent initiatives and developments in precision medicine call for appropriate prognostic tools to assist individualized or personalized approaches in cancer diagnosis and treatment. Data generated by clinical trials and medical research often include correlated longitudinal marker measurements and time- to-event information, which are possibly a recurrent event, competing risks, and a survival outcome. Primary interests of joint modeling include the association between the longitudinal marker measurements and time-to-event data, as well as predictions of survival probabilities of new observational units from the same population. …

Spatio-Temporal Analysis Of The Occupational Fatal Victimization Of Law Enforcement Officers In The Us, Xueyi Xing

Theses and Dissertations

The models with constant coefficients of the covariates across space and time are commonly used in spatio-temporal analyses. However, the associations between risk factors and the outcome could have locally differential temporal trends in many cases. In this study, a Bayesian latent cluster modeling strategy is employed to identify potential spatial clusters in which locally specific sets of temporally varying coefficients of covariates are allowed. A state-level panel data of police officers occupational fatal victimization for the years 1979-2010 is used. To accommodate overdisperson and excess zeros, a negative binomial model and zero-inflated Poisson/negative binomial models are also utilized. A …

Regression Models For Count Data Based On The Double Poisson Distribution, Rebecca Wardrop

Theses and Dissertations

This paper explores the double Poisson distribution. The probability mass function and the difficulties associated with derivative-based optimization for this distribution are discussed. Stata software developed for estimation of double Poisson regression is detailed. Simulations are used to test the software. Data which are over-, under-, and equidispersed relative to the Poisson are generated and the software is utilized to estimate a regression model, a zero-inflated model, and a marginalized zero-inflated model all based on the double Poisson distribution. The estimated power of the test for φ = 1 for the double Poisson models are compared to the power of …

Semiparametric Estimation Methods For Complex Accelerated Failure Time Model, Yinding Wang

Theses and Dissertations

The proportional hazards (PH) model and the accelerated failure time (AFT) model are the two most popular survival models in fitting the right-censored data. The AFT model is a useful alternative to the PH model, particularly when the PH assumption is not satisfied. Usually, the linear association is assumed with logarithm of survival time in the AFT model. However, the nonlinear association may exist in practice. The first project aims to handle the nonlinear component in the AFT model, which is called the semiparametric additive partial accelerated failure time (AP-AFT) model. Two estimation methods based on the rank-smooth method and …

Frailty Probit Models For Clustered Interval-Censored Failure Time Data, Haifeng Wu

Theses and Dissertations

Survival analysis is an important branch of statistics that deals with time to event data or survival data. An important feature of such data is that the survival time of interest is usually not completely known but is censored due to the design of the study or an early dropout. In this dissertation we focus on studying clustered interval-censored data, a special type of survival data. Interval-censored data arise in many epidemiological, social science, and medical studies, in which subjects are examined at periodical follow-up visits. The survival (or failure) time of interest is never exactly observed but is known …

A Weighted Gene Co-Expression Network Analysis For Streptococcus Sanguinis Microarray Experiments, Erik C. Dvergsten

Theses and Dissertations

Streptococcus sanguinis is a gram-positive, non-motile bacterium native to human mouths. It is the primary cause of endocarditis and is also responsible for tooth decay. Two-component systems (TCSs) are commonly found in bacteria. In response to environmental signals, TCSs may regulate the expression of virulence factor genes.

Gene co-expression networks are exploratory tools used to analyze system-level gene functionality. A gene co-expression network consists of gene expression profiles represented as nodes and gene connections, which occur if two genes are significantly co-expressed. An adjacency function transforms the similarity matrix containing co-expression similarities into the adjacency matrix containing connection strengths. Gene …

Provision Of Hospital-Based Palliative Care And The Impact On Organizational And Patient Outcomes, Marisa L. Roczen

Theses and Dissertations

Hospital-based palliative care services aim to streamline medical care for patients with chronic and potentially life-limiting illnesses by focusing on individual patient needs, efficient use of hospital resources, and providing guidance for patients, patients’ families and clinical providers toward making optimal decisions concerning a patient’s care. This study examined the nature of palliative care provision in U.S. hospitals and its impact on selected organizational and patient outcomes, including hospital costs, length of stay, in-hospital mortality, and transfer to hospice. Hospital costs and length of stay are viewed as important economic indicators. Specifically, lower hospital costs may increase a hospital’s profit …

In-Shoe Plantar Pressure System To Investigate Ground Reaction Force Using Android Platform, Ahmed A. Mostfa

Theses and Dissertations

Human footwear is not yet designed to optimally relieve pressure on the heel of the foot. Proper foot pressure assessment requires personal training and measurements by specialized machinery. This research aims to investigate and hypothesize about Preferred Transition Speed (PTS) and to classify the gait phase of explicit variances in walking patterns between different subjects. An in-shoe wearable pressure system using Android application was developed to investigate walking patterns and collect data on Activities of Daily Living (ADL). In-shoe circuitry used Flexi-Force A201 sensors placed at three major areas: heel contact, 1st metatarsal, and 5th metatarsal with a PIC16F688 microcontroller …

Selecting Spatial Scale Of Area-Level Covariates In Regression Models, Lauren Grant

Theses and Dissertations

Studies have found that the level of association between an area-level covariate and an outcome can vary depending on the spatial scale (SS) of a particular covariate. However, covariates used in regression models are customarily modeled at the same spatial unit. In this dissertation, we developed four SS model selection algorithms that select the best spatial scale for each area-level covariate. The SS forward stepwise, SS incremental forward stagewise, SS least angle regression (LARS), and SS lasso algorithms allow for the selection of different area-level covariates at different spatial scales, while constraining each covariate to enter at most one spatial …

Meta-Analytic Estimation Techniques For Non-Convergent Repeated-Measure Clustered Data, Aobo Wang

Theses and Dissertations

Clustered data often feature nested structures and repeated measures. If coupled with binary outcomes and large samples (>10,000), this complexity can lead to non-convergence problems for the desired model especially if random effects are used to account for the clustering. One way to bypass the convergence problem is to split the dataset into small enough sub-samples for which the desired model convergences, and then recombine results from those sub-samples through meta-analysis. We consider two ways to generate sub-samples: the K independent samples approach where the data are split into k mutually-exclusive sub-samples, and the cluster-based approach where naturally existing …

Dimension Reduction And Variable Selection, Hossein Moradi Rekabdarkolaee

Theses and Dissertations

High-dimensional data are becoming increasingly available as data collection technology advances. Over the last decade, significant developments have been taking place in high-dimensional data analysis, driven primarily by a wide range of applications in many fields such as genomics, signal processing, and environmental studies. Statistical techniques such as dimension reduction and variable selection play important roles in high dimensional data analysis. Sufficient dimension reduction provides a way to find the reduced space of the original space without a parametric model. This method has been widely applied in many scientific fields such as genetics, brain imaging analysis, econometrics, environmental sciences, etc. …

A New Right Tailed Test Of The Ratio Of Variances, Elizabeth Rochelle Lesser

UNF Graduate Theses and Dissertations

It is important to be able to compare variances efficiently and accurately regardless of the parent populations. This study proposes a new right tailed test for the ratio of two variances using the Edgeworth’s expansion. To study the Type I error rate and Power performance, simulation was performed on the new test with various combinations of symmetric and skewed distributions. It is found to have more controlled Type I error rates than the existing tests. Additionally, it also has sufficient power. Therefore, the newly derived test provides a good robust alternative to the already existing methods.

Macroconstants Of Development: A New Benchmark For The Strategic Development Of Advanced Countries And Firms, Andrey V. Bystrov, Vyacheslav N. Yusim, Tamilla Curtis

Publications

This research proposed a new indicator of countries’ development called “macroconstants of development”. The literature review indicates that the concept of "macroconstants of development" is not used at the moment in neither the theory nor the practice of industrial policy. Research of longitudinal data of total GDP, GDP per capita and their derivatives for most countries of the world was conducted. An analysis of statistical information has been done by employing econometric analyses.

Based on the analysis of the statistical data, which characterizes the development of large, technologically advanced countries in ordinary conditions, it was identified that the average acceleration …