Statistics and Probability Commons^™

Stochastic Modeling And Analysis Of Energy Commodity Spot Price Processes, Olusegun Michael Otunuga

USF Tampa Graduate Theses and Dissertations

Supply and demand in the World oil market are balanced through responses to price movement with considerable complexity in the evolution of underlying supply-demand

expectation process. In order to be able to understand the price balancing process, it is important to know the economic forces and the behavior of energy commodity spot price processes. The relationship between the different energy sources and its utility together with uncertainty also play a role in many important energy issues.

The qualitative and quantitative behavior of energy commodities in which the trend in price of one commodity coincides with the trend in price of …

Trend Analysis And Modeling Of Health And Environmental Data: Joinpoint And Functional Approach, Ram C. Kafle

USF Tampa Graduate Theses and Dissertations

The present study is divided into two parts: the first is on developing the statistical analysis and modeling of mortality (or incidence) trends using Bayesian joinpoint regression and the second is on fitting differential equations from time series data to derive the rate of change of carbon dioxide in the atmosphere.

Joinpoint regression model identifies significant changes in the trends of the incidence, mortality, and survival of a specific disease in a given population. Bayesian approach of joinpoint regression is widely used in modeling statistical data to identify the points in the trend where the significant changes occur. The purpose …

Statistical Analysis, Modeling, And Algorithms For Pharmaceutical And Cancer Systems, Bong-Jin Choi

USF Tampa Graduate Theses and Dissertations

The aim of the present study is to develop a statistical algorithm and model associ- ated with breast and lung cancer patients. In this study, we developed several statistical softwares, R packages, and models using our new statistical approach.

In the present study, we used the five parameters logistic model for determining the optimal doses of a pharmaceutical drugs, including dynamic initial points, an automatic process for outlier detection and an algorithm that develops a graphic user interface(GUI) program. The developed statistical procedure assists medical scientists by reducing their time in determining the optimal dose of new drugs, and can …

Probabilistic Modeling Of Lava Flows: A Hazard Assessment For The San Francisco Volcanic Field, Arizona, Aleeza Harburger

USF Tampa Graduate Theses and Dissertations

This study serves as a first step towards a comprehensive hazard assessment for the San Francisco volcanic field in northern Arizona, which can be applied to local response plans and educational initiatives. The primary goal of this thesis is to resolve the conditional probability that, given a lava flow effusing from a new vent in the San Francisco volcanic field, it will inundate the city limits of Flagstaff. The spatial distribution of vents within the San Francisco volcanic field was analyzed in order to execute a lava flow simulation to determine the inundation hazard to Flagstaff. The Gaussian kernel function …

A Monte Carlo Study: The Consequences Of The Misspecification Of The Level-1 Error Structure, Merlande Petit-Bois

USF Tampa Graduate Theses and Dissertations

Single-case interventions allow for the repeated measurement of a case or participant across multiple time points, to assess the treatment¡͞s effect on one specific case or participant. The basic interrupted time series design includes two phases: baseline and treatment. Raudenbush and Byrk (2002) demonstrated that a meta-analysis of large group designs can be seen as a special case of multi-level analysis with participants (level-one) nested within studies (level-two). Raw data from a set of single case design studies have a similar structure. Van den Noortgate and Onghena (2003) illustrated the use of a two-level model to analyze data in primary …

Location And Capacity Modeling Of Network Interchanges, Aldo D. Fabregas

USF Tampa Graduate Theses and Dissertations

Network design decisions, especially those pertaining to urban infrastructure, are made by a central authority or network leader, and taking into consideration the network users or followers. These network decision problems are formulated as non-linear bi-level programming problems. In this work, a continuous network design problem (CNDP) and discrete network design problem (DNDP) bi-level optimization programs are proposed and solved in the context of transportation planning. The solution strategy involved reformulation and linearization as a single-level program by introducing the optimality conditions of the lower level problem into the upper level problem. For the CNDP, an alternative linearization algorithm (modified …

Bayesian Estimation Of Panel Data Fractional Response Models With Endogeneity: An Application To Standardized Test Rates, Lawrence Kessler

USF Tampa Graduate Theses and Dissertations

In this paper I propose Bayesian estimation of a nonlinear panel data model with a fractional dependent variable (bounded between 0 and 1). Specifically, I estimate a panel data fractional probit model which takes into account the bounded nature of the fractional response variable. I outline estimation under the assumption of strict exogeneity as well as when allowing for potential endogeneity. Furthermore, I illustrate how transitioning from the strictly exogenous case to the case of endogeneity only requires slight adjustments. For comparative purposes I also estimate linear specifications of these models and show how quantities of interest such as marginal …

Statistical Topics Applied To Pressure And Temperature Readings In The Gulf Of Mexico, Malena Kathleen Allison

USF Tampa Graduate Theses and Dissertations

The field of statistical research in weather allows for the application of old and new methods, some of which may describe relationships between certain variables better such as temperatures and pressure. The objective of this study was to apply a variety of traditional and novel statistical methods to analyze data from the National Data Buoy Center, which records among other variables barometric pressure, atmospheric temperature, water temperature and dew point temperature. The analysis included attempts to better describe and model the data as well as to make estimations for certain variables. The following statistical methods were utilized: linear regression, non-response …

Effectiveness Of Propensity Score Methods In A Multilevel Framework: A Monte Carlo Study, Aarti P. Bellara

USF Tampa Graduate Theses and Dissertations

Propensity score analysis has been used to minimize the selection bias in observational studies to identify causal relationships. A propensity score is an estimate of an individual's probability of being placed in a treatment group given a set of covariates. Propensity score analysis aims to use the estimate to create balanced groups, akin to a randomized experiment. This study used Monte Carlo methods to examine the appropriateness of using propensity score methods to achieve balance between groups on observed covariates and reproduce treatment effect estimates in multilevel studies. Specifically, this study examined the extent to which four different propensity score …

Uncontrolled Hypertension And Associated Factors In Hypertensive Patients At The Primary Healthcare Center Luis H. Moreno, Panama: A Feasibility Study, Roderick Ramon Chen Camano

USF Tampa Graduate Theses and Dissertations

Background: According to the World Health Organization (WHO), hypertension is a major risk factor for cardiovascular disease (CVD), renal impairment, peripheral vascular disease, and blindness. In Panama, a recent study estimated the prevalence of hypertension at 38.5% in the two main provinces of the country, with a rate of uncontrolled hypertension of 47.2%.

Objectives: The aims of this study were to assess the feasibility of the study design and to describe the characteristics of the hypertensive population and the physician's adherence to Panamanian antihypertensive protocols and their relationship with uncontrolled hypertension.

Methods: This is a cross-sectional study of adult hypertensive …

Multiple Calibrations In Integrative Data Analysis: A Simulation Study And Application To Multidimensional Family Therapy, Kristin Wynn Hall

USF Tampa Graduate Theses and Dissertations

A recent advancement in statistical methodology, Integrative Data Analyses (IDA Curran & Hussong, 2009) has led researchers to employ a calibration technique as to not violate an independence assumption. This technique uses a randomly selected, simplified correlational structured subset, or calibration, of a whole data set in a preliminary stage of analysis. However, a single calibration estimator suffers from instability, low precision and loss of power. To overcome this limitation, a multiple calibration (MC; Greenbaum et al., 2013; Wang et al., 2013) approach has been developed to produce better estimators, while still removing a level of dependency in the data …

A Latent Mixture Approach To Modeling Zero-Inflated Bivariate Ordinal Data, Rajendra Kadel

USF Tampa Graduate Theses and Dissertations

Multivariate ordinal response data, such as severity of pain, degree of disability, and satisfaction with a healthcare provider, are prevalent in many areas of research including public health, biomedical, and social science research. Ignoring the multivariate features of the response variables, that is, by not taking the correlation between the errors across models into account, may lead to substantially biased estimates and inference. In addition, such multivariate ordinal outcomes frequently exhibit a high percentage of zeros (zero inflation) at the lower end of the ordinal scales, as compared to what is expected under a multivariate ordinal distribution. Thus, zero inflation …

Tracking Atlantic Hurricanes Using Statistical Methods, Elizabeth Caitlin Miller

USF Tampa Graduate Theses and Dissertations

Creating an accurate hurricane location forecasting model is of the utmost importance because of the safety measures that need to occur in the days and hours leading up to a storm's landfall. Hurricanes can be incredibly deadly and costly, but if people are given adequate warning, many lives can be spared. This thesis seeks to develop an accurate model for predicting storm location based on previous location, previous wind speed, and previous pressure. The models are developed using hurricane data from 1980-2009.

Statistical Analysis And Modeling Of Brain Tumor Data: Histology And Regional Effects, Keshav Prasad Pokhrel

USF Tampa Graduate Theses and Dissertations

Comprehensive statistical models for non-normally distributed cancerous tumor sizes are

of prime importance in epidemiological studies, whereas a long term forecasting models

can facilitate in reducing complications and uncertainties of medical progress. The statistical

forecasting models are critical for a better understanding of the disease and supply

appropriate treatments. In addition such a model can be used for the allocations of budgets,

planning, control and evaluations of ongoing efforts of prevention and early detection of

the diseases.

In the present study, we investigate the effects of age, demography, and race on primary

brain tumor sizes using quantile regression methods to …

Optimization In Non-Parametric Survival Analysis And Climate Change Modeling, Iuliana Teodorescu

USF Tampa Graduate Theses and Dissertations

Many of the open problems of current interest in probability and statistics involve complicated data

sets that do not satisfy the strong assumptions of being independent and identically distributed. Often,

the samples are known only empirically, and making assumptions about underlying parametric

distributions is not warranted by the insufficient information available. Under such circumstances,

the usual Fisher or parametric Bayes approaches cannot be used to model the data or make predictions.

However, this situation is quite often encountered in some of the main challenges facing statistical,

data-driven studies of climate change, clinical studies, or financial markets, to name a few. …

Statistical Analysis And Modeling Of Prostate Cancer, Yiu Ming Chan

USF Tampa Graduate Theses and Dissertations

The objective of the present study is to address some important questions related to prostate cancer treatments and survivorship among White and African American men. It is commonly understood that the risk of developing prostate cancer is higher in African American men than the other races. However, using parametric analysis, this study demonstrates that this perception is a "myth" not a "reality". The study further identifies the existence of racial/ethnic disparities by comparing the average mean tumor size, the median of survival time, and the survival function between White and African American men. These results underline the necessity of understanding …

A Monte Carlo Approach To Change Point Detection In A Liver Transplant, Alexia Melissa Makris

USF Tampa Graduate Theses and Dissertations

Patient survival post liver transplant (LT) is important to both the patient and the center's accreditation, but over the years physicians have noticed that distant patients struggle with post LT care. I hypothesized that patient's distance from the transplant center had a detrimental effect on post LT survival. I suspected Hepatitis C (HCV) and Hepatocellular Carcinoma (HCC) patients would deteriorate due to their recurrent disease and there is a need for close monitoring post LT. From the current literature it was not clear if patients' distance from a transplant center affects outcomes post LT. Firozvi et al. (Firozvi AA, 2008) …

Age Dependent Analysis And Modeling Of Prostate Cancer Data, Nana Osei Mensa Bonsu

USF Tampa Graduate Theses and Dissertations

Growth rate of prostate cancer tumor is an important aspect of understanding the natural history of prostate cancer. Using real prostate cancer data from the SEER database with tumor size as a response variable, we have clustered the cancerous tumor sizes into age groups to enhance its analytical behavior. The rate of change of the response variable as a function of age is given for each cluster. Residual analysis attests to the quality of the analytical model and the subject estimates. In addition, we have identified the probability distribution that characterize the behavior of the response variable and proceeded with …

The Positive Illusory Bias And Adhd Symptoms: A New Measurement Approach, Sarah A. Fefer

USF Tampa Graduate Theses and Dissertations

The purpose of this study was to investigate perceptions of academic and social competence among adolescents with a continuum of inattentive and hyperactive/impulsive symptoms. Past literature suggests that children with Attention-Deficit/Hyperactivity Disorder (ADHD) display self-perceptions that are overly positive compared to external indicators of competence, a phenomenon that is referred to as the positive illusory bias (PIB; Owens, Goldfine, Evangelista, Hoza, & Kaiser, 2007). The PIB is well supported among children with ADHD, and recent research suggests that the PIB persists into adolescence. To date, research on the PIB has relied on difference scores (i.e., an indicator of competence is …

Measuring Technical Efficiency Of The Japanese Professional Football (Soccer) League (J1 And J2), Dan Zhao

USF Tampa Graduate Theses and Dissertations

This is the first paper to measure the efficiency of the Japan Professional Football League clubs both the first and the second divisions. In Chapter 1, a non-parametric method Data Envelopment Development (DEA) is used and the data covers six seasons from 2005 to 2010. The input variables are payroll, cost besides payroll, and total assets. The output variables are attendance, revenue, and points awarded. I use different output combinations in order to check the sensitivity of the efficiency of the clubs after the original composition. This is also the first research to include more than one division of the …

Linear Mixed-Effects Models: Applications To The Behavioral Sciences And Adolescent Community Health, Lizmarie Gabriela Maldonado

USF Tampa Graduate Theses and Dissertations

Linear mixed-effects (LME) modeling is a widely used statistical method for analyzing repeated measures or longitudinal data. Such longitudinal studies typically aim to investigate and describe the trajectory of a desired outcome. Longitudinal data have the advantage over cross-sectional data by providing more accuracy for the model. LME models allow researchers to account for random variation among individuals and between individuals.

In this project, adolescent health was chosen as a topic of research due to the many changes that occur during this crucial time period as a precursor to overall well-being in adult life. Understanding the factors that influence how …

Statistical Content In Middle Grades Mathematics Textbooks, Maria Consuelo (Suzie) Capiral Pickle

USF Tampa Graduate Theses and Dissertations

Statistical Content in Middle Grades Mathematics Textbooks

Maria Consuelo (Suzie) Capiral Pickle

Abstract

This study analyzed the treatment and scope of

statistical concepts in four, widely-used, contemporary, middle

grades mathematics textbook series: Glencoe Math Connects,

Prentice Hall Mathematics, Connected Mathematics Project, and

University of Chicago School Mathematics Project. There were

three phases for the data analysis. Phase 1 addressed the location

and sequence of the statistical concepts. Phase 2 focused upon an

examination of the lesson narrative, its components and scope.

Phase 3 analyzed the level of cognitive demand required of the

students to complete the exercises, and the total …

Statistical Estimation Of Physiologically-Based Pharmacokinetic Models: Identifiability, Variation, And Uncertainty With An Illustration Of Chronic Exposure To Dioxin And Dioxin-Like-Compounds., Zachary John Thompson

USF Tampa Graduate Theses and Dissertations

Assessment of human exposure to environmental chemicals is inherently subject to uncertainty and variability. There are data gaps concerning the inventory, source, duration, and intensity of exposure

as well as knowledge gaps regarding pharmacokinetics in general. These gaps result in uncertainties in exposure assessment.

The uncertainties compound further with variabilities due to population variations regarding stage of life, life style, and susceptibility,

etc. Use of physiologically-based pharmacokinetic (PBPK) models promises to reduce the uncertainties and enhance extrapolation between species, between routes, from high to low dose, and from acute to chronic exposure. However, fitting PBPK models is challenging because of …

Stochastic Modeling Of Network-Centric Epidemiological Processes, Divine Wanduku

USF Tampa Graduate Theses and Dissertations

The technological changes and educational expansion have created the heterogeneity in the human species. Clearly, this heterogeneity generates a structure in the population

dynamics, namely: citizen, permanent resident, visitor, and etc. Furthermore, as the heterogeneity in the population increases, the human mobility between meta-populations patches

also increases. Depending on spatial scales, a meta-population patch can be decomposed into sub-patches, for examples: homes, neighborhoods, towns, etc. The dynamics of human

mobility in a heterogeneous and scaled structured population is still its infancy level. We develop and investigate (1) an algorithmic two scale human mobility dynamic model for a meta-population. Moreover,the two …

Evaluation Of Repeated Biomarkers: Non-Parametric Comparison Of Areas Under The Receiver Operating Curve Between Correlated Groups Using An Optimal Weighting Scheme, Ping Xu

USF Tampa Graduate Theses and Dissertations

Receiver Operating Characteristic (ROC) curves are often used to evaluate the prognostic performance of a continuous biomarker. In a previous research, a non-parametric ROC approach was introduced to compare two biomarkers with repeated measurements. An asymptotically normal statistic, which contains the subject-specific weights, was developed to estimate the areas under the ROC curve of biomarkers. Although two weighting schemes were suggested to be optimal when the within subject correlation is 1 or 0 by the previous study, the universal optimal weight was not determined. We modify this asymptotical statistic to compare AUCs between two correlated groups and propose a solution …

Bayesian Inference On Mixed-Effects Models With Skewed Distributions For Hiv Longitudinal Data, Ren Chen

USF Tampa Graduate Theses and Dissertations

Statistical models have greatly improved our understanding of the pathogenesis of HIV-1 infection

and guided for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies.

Although various statistical modeling and analysis methods have been applied for estimating the

parameters of HIV dynamics via mixed-effects models, a common assumption of distribution is

normal for random errors and random-effects. This assumption may lack the robustness against

departures from normality so may lead misleading or biased inference. Moreover, some covariates

such as CD4 cell count may be often measured with substantial errors. Bivariate clustered

(correlated) data are also commonly encountered in …

Statistical Modeling And Analysis Of Breast Cancer And Pancreatic Cancer, Zahra Kottabi

USF Tampa Graduate Theses and Dissertations

Abstract

The object of the present study is to apply statistical modeling and estimate the mean of optimism of breast cancer patients as function of attribute variables; delay, education and age for each race of breast cancer patients. Moreover, to investigate the nonlinear association between optimism, education, age and delay with respect to each race and both. Furthermore, to develop differential equations that will characterize the behavior of the pancreatic cancer tumor size as a function of time. Having such differential equations, the mean solution of which once plotted will identify the rate of change of tumor size as a …

Multi-Time Scales Stochastic Dynamic Processes: Modeling, Methods, Algorithms, Analysis, And Applications, Jean-Claude Pedjeu

USF Tampa Graduate Theses and Dissertations

By introducing a concept of dynamic process operating under multi-time scales in sciences and engineering, a mathematical model is formulated and it leads to a system of multi-time scale stochastic differential equations. The classical Picard-Lindel\"{o}f successive approximations scheme is expended to the model validation problem, namely, existence and uniqueness of solution process. Naturally, this generates to a problem of finding closed form solutions of both linear and nonlinear multi-time scale stochastic differential equations. To illustrate the scope of ideas and presented results, multi-time scale stochastic models for ecological and epidemiological processes in population dynamic are exhibited. Without loss in generality, …

Stochastic Hybrid Dynamic Systems: Modeling, Estimation And Simulation, Daniel Siu

USF Tampa Graduate Theses and Dissertations

Stochastic hybrid dynamic systems that incorporate both continuous and discrete dynamics have been an area of great interest over the recent years. In view of applications, stochastic hybrid dynamic systems have been employed to diverse fields of studies, such as communication networks, air traffic management, and insurance risk models. The aim of the present study is to investigate properties of some classes of stochastic hybrid dynamic systems.

The class of stochastic hybrid dynamic systems investigated has random jumps driven by a non-homogeneous Poisson process and deterministic jumps triggered by hitting the boundary. Its real-valued continuous dynamic between jumps is described …

Decision Aid Models For Resource Sharing Strategies During Global Influenza Pandemics, Alfredo Santana Reynoso

USF Tampa Graduate Theses and Dissertations

Pandemic influenza outbreaks have historically entailed significant societal and economic disruptions. Today, our quality of life is threatened by our inadequate preparedness for the imminent pandemic. The key challenges we are facing stem from a significant uncertainty in virus epidemiology, limited response resources, inadequate international collaboration, and the lack of appropriate science-based decision support tools. The existing literature falls short of comprehensive models for global pandemic spread and mitigation which incorporate the heterogeneity of the world regions and realistic travel networks. In addition, there exist virtually no studies which quantify the impact of resource sharing strategies among multiple countries. This …