Statistics and Probability Commons^™

Statistical Challenges And Simulation Results For Pilot Clinical Trials, Weiliang Cen

USF Tampa Graduate Theses and Dissertations

Background: The effect size estimated from a pilot trial is often an inaccurate reflection of the true effect size observed in a large trial, leading to either underestimation or overestimation. Published data suggest that effect sizes from large trials are typically smaller than those reported in their corresponding pilot trials. To address this discrepancy, conservative or discount adjustment methods are widely recommended to modify pilot trial effect sizes when calculating sample sizes, thereby maintaining adequate statistical power. This study aims to assess effect sizes from both pilot and large trials and to evaluate the performance of existing adjustment methods.

Methods: …

Textbook Content Analysis For Statistical Content Standards For 8th-Grade Math: Commercial Publishers, Curriculum Software Supplement, And An Open Educational Resource, Matthew M. O'Brien

USF Tampa Graduate Theses and Dissertations

This dissertation examined the vertical and horizontal content analysis of statistical content within current 8th-grade mathematics instructional materials to determine the extent to which students are provided with opportunity to learn high cognitive instances and the usage of the four phases of statistical problem-solving across two commercial publishers, one open educational resource, and one curriculum software supplement. All previous analyses of statistics education content textbooks have only examined textbooks from commercial publishers or those published by national governments. The horizontal content textbook analysis investigated the textbook's background information and overall structure. The vertical content textbook analysis examined the cognitive level …

Essays On Information Technology In Healthcare, Gleb Zavadskiy

USF Tampa Graduate Theses and Dissertations

Information technologies (IT) and information systems (IS) have profound significance across various sectors of society, including healthcare, business, education, government, and beyond. First, IT facilitates instant communication globally through email, messaging apps, video conferencing, and social media, revolutionizing how individuals and organizations interact, collaborate, and share information (Hacker et al. 2020; Tang and Hew 2020).

Secondly, the Internet and digital libraries provide worldwide access to vast amounts of information, what makes knowledge and education available to everyone, empowering individuals to learn and stay informed on diverse topics (Haleem et al. 2022). Another aspect of IT systems in various industries is …

Artificial Intelligence Modeling Of Alzheimer's Disease And Environmental Science, Mohamed Abu Sheha

USF Tampa Graduate Theses and Dissertations

A data-driven statistical model operates as a mathematical illustration of a tangible issuefaced in reality, enabling the creation of predictions or decisions based on data. The process of utilizing probability and statistical principles in statistical models is essential for deriving meaningful conclusions from the data. The research conducted in this dissertation utilizes artificial intelligence (AI) models to integrate findings across health and environmental sci- ence.

The first research study in this dissertation, Alzheimer’s disease is a mental health issue and a brain aging dilemma that makes it difficult for older people to complete daily tasks without assistance. The physician uses …

Mathematical Modeling Of Tumor Response Dynamics To Predict Progression-Free Survival In Patients With Recurrent High-Grade Glioma, Daniel James Glazar

USF Tampa Graduate Theses and Dissertations

In this dissertation, I aim to develop a mathematical model describing tumor volume response dynamics to perform individual dynamic predictions of progression-free survival (PFS) on patients with recurrent high-grade glioma (rHGG).

Patients with rHGG have a dismal prognosis with median overall survival (OS) of <12 months and median PFS of <7 months. However, there is a wide heterogeneity in treatment responses. Therefore, to aid clinicians with making decisions to alter therapeutic protocol, I would like to predict patient-specific PFS.

To perform individual dynamic predictions, I employ the Claret tumor growth inhibition (TGI) model. I further develop this model by coupling it with two different survival models. Inter-patient heterogeneity is also taken into account through mixed effects, including covariate effects. Model PFS predictions were evaluated using receiver operating characteristic (ROC) curve analysis as well as Brier …

Stochastic Analytical Predictive Models For Life Sciences And Crop Production Process, Erasmus Tetteh-Bator

USF Tampa Graduate Theses and Dissertations

Analytical predictive modeling uses algorithm-based mathematical, probabilistic and statistical methods to anticipate future events by identifying patterns in past data. It is a technique for predicting outcomes and a key application of statistical analysis in real-world scenarios. Real data-driven predictive models in various fields, such as life sciences, economics,or production industry, help individuals and institutions make data-driven informed decisions, which is essential for business success, providing companies with a competitive edge.

One of every five adult deaths is caused by heart disease and one person dies every 33 from cardiovascular disease in the United States according to the Center for …

Comparative Analysis Of Time Series Models On U.S. Stock And Exchange Rates: Bayesian Estimation Of Time Series Error Term Model Versus Machine Learning Approaches, Young Keun Yang

USF Tampa Graduate Theses and Dissertations

This study presents a comparative analysis of contemporary applications of time series models, focusing on the Bayesian approach. In contrast to many nonparametric studies, the Bayesian approach circumvents the common issue of bandwidth selection by offering systematic estimation and avoiding ad hoc methods. Specifically, we delve into the Bayesian approach for estimating the autocovariance function of a time series model’s error term. Traditional time series models often make the unrealistic assumption of a constant error term. Furthermore, models such as autoregressive conditional heteroskedasticity (ARCH) and general autoregressive conditional heteroskedasticity (GARCH) address the limitation of constant variance by assuming an autoregressive …

Advancing Text Summarization And Classification: Deep Insights From Transformer-Based Statistical Learning, Kun Bu

USF Tampa Graduate Theses and Dissertations

Artificial Intelligence (AI) is a part of human's daily life nowadays. Machine Learning (ML) as one aspect from AI has been rapidly developing during the past two decades, especially from the statistical learning approaches, which emphasized the use of probability and statistics to model data, such as Support Vector Machines (SVMs) for classification and regression tasks to the ensemble learning techniques, such as Random Forest, Gradient Boosting Machine (GBM), and stacking. Ensemble learning has evolved into a pivotal concept in contemporary machine learning, empowering practitioners to amalgamate multiple models to enhance generalization, accuracy, and robustness. As the field of machine …

Utilizing Machine Learning Techniques For Accurate Diagnosis Of Breast Cancer And Comprehensive Statistical Analysis Of Clinical Data, Myat Ei Ei Phyo

USF Tampa Graduate Theses and Dissertations

Breast cancer represents a formidable malignancy, presenting a substantial threat to global health and individual well-being. Conventionally, it is widely held that the prognosis for breast cancer patients hinges predominantly upon the timing of diagnosis and the extent of cancer progression, typically delineated by its stage. However, emerging evidence from robust regression and machine learning analyses challenges this prevailing notion. The results indicate that survival months cannot be solely attributed to diagnosis and socio-economic factors. Instead, additional variables such as existing diseases and treatment complexities may contribute to the intricate landscape of breast cancer outcomes.

This research aims to delve …

Using Social Network Analysis To Measure And Visualize Student Clustering Within Middle And High Schools, Geoffrey David West

USF Tampa Graduate Theses and Dissertations

The dominant philosophy of American public schools has been to group students together based on similar characteristics. Known as tracking, high achieving students would take courses on the “college track” while others would take “career track” courses. It was not long until advocates noticed that this process unfairly advantaged affluent and White student over poor and minoritized groups. A new process called “ability grouping” took over where tracking left off, but to the same effect. It is difficult to measure the degree students are grouped together by a certain characteristic, and while a few research papers aim to do so, …

Cybersecurity: Stochastic Intensity Function And Monitoring Indicators, Hackers Demographics, Statistical Analysis And Treatment Of Ovarian Cancer, Ranju Karki

USF Tampa Graduate Theses and Dissertations

One of the major tasks in the present day-era is securing computer systems against unauthorized access. Every year we lost millions of dollars because of cyber attacks and thousands of people suffer economically and psychologically. Rapid development in the field of information technology increases the challenges to the Information technology personnel working on protecting against cyber attacks. In cyber security, vulnerabilities and hackers play a major role. Researchers are putting in enormous efforts to develop methods and models to control vulnerabilities and psychological behavior and motivation of hackers. Our study defines two important aspects of the computer operating system concerning …

Real Data–Driven Analytical Predictive Modeling For Financial Systems: Stochastic Intensity Function And Monitoring Indicator, Jayanta Kumar Pokharel

USF Tampa Graduate Theses and Dissertations

Data contain important information and data driven decision making process is the most for any type of businesses to succeed. It is the fact that businesses which follow data driven decision making process will have competitive edge over their counterparts which contributes in value creation for any firm or individual. Statistical procedure and methodology is the heart of data science which helps to extract crucial information from the data scientifically. It is common technique to use algorithm on historical data to see the outcome for the future which we defined as “prediction”, and predictive modeling is one of major statistical …

Exploring Time-Varying Extraneous Variables Effects In Single-Case Studies, Ke Cheng

USF Tampa Graduate Theses and Dissertations

The effect of time-varying extraneous variables has been studied in other statistical analyses such as using Kaplan–Meier or Cox regression analysis in survival analyses. Nonetheless, the effect of modeling versus not modeling individual specific time varying extraneous variables has not been explored in multiple-baseline single case designs through Monte Carlo simulation studies. Therefore, in my dissertation, I used simulation methods to explore for a variety of conditions (varying in the number of participants, number of observations per participant, type of extraneous variable effect, size of the true intervention effect) the impact of extraneous variables on bias and standard error of …

Statistical Analysis Of Ribonucleotide Incorporation In Human Cells, Tejasvi Channagiri

USF Tampa Graduate Theses and Dissertations

During the DNA replication process, ribonucleotides, the building blocks of RNA, may be occasionally incorporated in the newly synthesized DNA. DNA is primarily composed of deoxyribonucleotides and there exist cellular mechanisms for removing ribonucleotides from DNA, which may point towards ribonucleotide incorporation being a replication error. Further, an excess of these ribonucleotides in the genome has been known to lead to genomic instability and has been implicated in human diseases. However, there are also hypotheses that suggest that ribonucleotides may be beneficial in certain circumstances. In this study we examine ribonucleotide incorporation in the human genome in several human cell …

Fuzzy Kc Clustering Imputation For Missing Not At Random Data, Markku A. Malmi Jr.

USF Tampa Graduate Theses and Dissertations

Research has a variety of difficulties, especially when involving human subjects, and one of the most prevalent is the issue of missing data. Missing data will always be present in research due to the fact there is no perfect method for collecting data and protecting against human error or mechanical failure. This requires researchers to be able to mitigate the problems that come along with missing data; reduction in power of an analysis and bias introduced by the missing pattern. This research investigated a non-parametric method using a nested approach of fuzzy K-Modes and fuzzy C-Means clustering to impute missing …

Statistical Methods For Reliability Test Planning And Data Analysis, Oluwaseun Elizabeth Otunuga

USF Tampa Graduate Theses and Dissertations

This dissertation develops several statistical methods to advance the techniques and applications in the fields of reliability test planning and data analysis as well as statistical modeling and analysis in survival analysis.

The first project focuses on developing new demonstration test plans for lifetime data based on considering multiple objectives. Reliability demonstration tests have been broadly used for assuring reliability performance at the desired confidence level. We consider lifetime data that follows a Weibull distribution which has been broadly used for modeling a variety of shapes of lifetime distributions. When planning a demonstration test, there are often multiple aspects to …

Exploring The Vulnerability Of A Neural Tangent Generalization Attack (Ntga) - Generated Unlearnable Cifar-10 Dataset, Gitte Ost

USF Tampa Graduate Theses and Dissertations

Nowadays, a massive amount of data is generated and stored on servers and cloudsfrom various applications daily. Preventing these data from unauthorized use often becomes necessary and critical in various real-world applications. Many researchers have studied this crucial problem and developed different methods for this purpose. Among them, Neural Tangent Generalization Attack (NTGA) is one of the most efficient methods to make a dataset unlearnable, which means that the dataset is not learnable by machine learning/deep learning methods. That is, the NTGA-generated dataset is protected against unauthorized use. In this thesis, we explore the vulnerability of an NTGA-generated unlearnable CIFAR-10 …

Association Between Use Of Remdesivir And Bradycardia, Gibret Umeukeje

USF Tampa Graduate Theses and Dissertations

Remdesivir received the first emergency use authorization from the FDA for the treatment of COVID-19. Multiple adverse drug reactions (ADR) have been reported since its approval in October 2020. Bradycardia, defined by a decrease in heart rate has been reported as an adverse event for patients receiving remdesivir for COVID-19 treatment. The purpose of the research is to systematically investigate the frequency of occurrence of bradycardia in adults receiving remdesivir using clinical data derived from the FDA Adverse Event Reporting System (FAERS) database. Patients receiving remdesivir were compared to those receiving Paxlovid, Regen-Cov, and Dexamethasone for COVID-19 treatment to see …

Joint Models For Repeated Measured, Non-Normally Distributed Multilevel Data, Weiwei He

USF Tampa Graduate Theses and Dissertations

Clinical trials have tended to collect both survival information and longitudinal biomarkers, as well as other covariates. In order to better assess the severity of diverse diseases, we need to collect various longitudinal outcomes. Furthermore, longitudinal data could consist of a number of different measurements of varying types. The multilevel item response theory (MLIRT) model has been widely used in several fields such as public health and health sciences for multivariate longitudinal outcomes. Joint models combining the longitudinal and survival processes, as well as the relation between them, have been developed to minimize bias and improve the efficiency of estimates. …

Data-Driven Analytical Predictive Modeling For Pancreatic Cancer, Financial & Social Systems, Aditya Chakraborty

USF Tampa Graduate Theses and Dissertations

Pancreatic cancer is one of the most deathly disease and becoming an increasingly commoncause of cancer mortality. It continues giving rise to massive challenges to clinicians and cancer researchers. The combined five-year survival rate for pancreatic cancer is extremely low, about 5 to 10 percent, owing to the fact that a large number of the patients are diagnosed at stage IV when the disease has metastasized. Our study investigates if there exists any statistical significant difference between the median survival times and also the survival probabilities of male and female pancreatic cancer patients at different cancer stages, and irrespective of …

Nonparametric Estimation Of Transition Probabilities In Illness-Death Model Based On Ranked Set Sampling, Ying Ma

USF Tampa Graduate Theses and Dissertations

The ranked set sampling (RSS) design is applied widely in agriculture, environmental science, and medical research where the exact measurements of sampling units is costly, but sampling units can be ranked by a correlated concomitant variable. RSS is usually a cost-efficient alternate to simple random sampling (SRS) for selecting more representative samples. This study presents a novel methodology to investigate the nonparametric estimation of transition probabilities in illness-death model using the RSS design. We study the Aalen–Johansen estimator of transition probabilities in illness-death Markov model based on RSS design under random right censoring time and propose nonparametric estimators of the …

New Developments In Statistical Optimal Designs For Physical And Computer Experiments, Damola M. Akinlana

USF Tampa Graduate Theses and Dissertations

Statistical design of experiments allows for multiple factors influencing a process to be systematically manipulated in an experiment, and their effects on the output of the process to be studied via statistical modeling and analysis. Classical designs offer general nice performance but have limited applications due to restricted design size, region, and randomization structure. Computer generated optimal designs become more popular in recent decades due to the rapid growth in computing power. Most existing work in optimal design of experiments involves designing experiments with optimal performance on a single chosen objective or a single response. However, with the increasing limitation …

Video Anomaly Detection: Practical Challenges For Learning Algorithms, Keval Doshi

USF Tampa Graduate Theses and Dissertations

Anomaly detection in surveillance videos is attracting an increasing amount of attention. Despite the competitive performance of several existing methods, they lack theoretical performance analysis, particularly due to the complex deep neural network architectures used in decision making. Additionally, real-time decision making is an important but mostly neglected factor in this domain. Much of the existing methods that claim to be online, depend on batch or offline processing in practice. Furthermore, several critical tasks such as continual learning, model interpretability and cross-domain adaptability are completely neglected in existing works. Motivated by these research gaps, in this dissertation we discuss our …

A Functional Optimization Approach To Stochastic Process Sampling, Ryan Matthew Thurman

USF Tampa Graduate Theses and Dissertations

The goal of the current research project is the formulation of a method for the estimation and modeling of additive stochastic processes with both linear- and cycle-type trend components as well as a relatively robust noise component in the form of Levy processes. Most of the research in stochastic processes tends to focus on cases where the process is stationary, a condition that cannot be assumed for the model above due to the presence of the cyclical sub-component in the overall additive process. As such, we outline a number of relevant theoretical and applied topics, such as stochastic processes and …

Adversarial And Data Poisoning Attacks Against Deep Learning, Jing Lin

USF Tampa Graduate Theses and Dissertations

Machine translation software, image captioning, grammar check (Grammarly), chatbot, real-time captioning and translation, music genre classification, and document classification are a few examples of deep learning applications that achieve outstanding performance in areas where traditional statistical techniques have difficulty performing classification and/or regression. Google translator has over 100 billion daily users and can translate 109 languages instantly (much faster than a human translator). AlphaGo won Lee Sedol, the eighteen-time world champion. Microsoft Team's living captioning provides accurate real-time captioning as a speaker speaks. Deep learning achieves undoubtedly remarkable performance. However, recent studies on adversarial attacks and data poisoning attacks show …

A Physically Constrained Wavelet-Aided Statistical Model (Pcwasm) For Multidecadal Groundwater Dynamics Predictions And Climate Change Evaluation, Fatih Gordu

USF Tampa Graduate Theses and Dissertations

Long-term groundwater management relies on forecasts of decadal or longer groundwater levels driven by nested scales of variability in climate. Discerning the impacts of anthropogenic stressors on groundwater is also critical for establishing predevelopment benchmark groundwater conditions and developing climate change adaptation strategies to reduce risks and increase resiliency. This research demonstrates the development and successful applications of a new methodology to predict and assess multidecadal groundwater dynamics for understanding long-term effects of climate change and groundwater withdrawals.

A Physically Constrained Wavelet-Aided Statistical Model (PCWASM) is first introduced to analyze and predict monthly aquifer levels on multidecadal time scales. The …

Using Fine-Scale Aquatic Habitat Data To Construct Dreissenid Sdms In The Laurentian Great Lakes, Grace C. Henderson

USF Tampa Graduate Theses and Dissertations

The invasion of the Laurentian Great Lakes by aquatic invasive species (AIS) has been the subject of investigation for decades, due to their dramatic alterations to the ecosystem and high economic costs. Two AIS with the largest impacts are dreissenid zebra and quagga mussels, and though these species have been studied extensively, questions remain about what factors control their distributions, and whether lake warming will alter these distributions. Species distribution models (SDMs) offer a powerful tool to examine the relationship between species presences and environmental variables, which are typically bioclimactic data. The creation of the Aquatic Habitat (AqHab) dataset containing …

Measurements Of Generalizability And Adjustment For Bias In Clinical Trials, Yuanyuan Lu

USF Tampa Graduate Theses and Dissertations

While randomized controlled trials (RCTs) are widely used as a gold standard in clinical research and public health, they are criticized because of a potential lack of generalizability, as the trial patients may be unrepresentative of the target patient population. Few research addresses how to assess and evaluate the generalizability of RCTs. As we know, patients are rarely selected on a random basis from a well-defined patient population of interest into a clinical trial. Generalizing findings from the RCT samples to the patient population has begun to receive increasing attention. We simulate a patient population with treatment effect size of …

Effective Statistical And Machine Learning Methods To Analyze Children's Vocabulary Learning, Houston T. Sanders

USF Tampa Graduate Theses and Dissertations

Poor methodological and statistical practices can lead to unreliable results. The collaboration between statisticians and researchers can remedy this. Early education intervention research rarely uses advanced statistical techniques. Within early education, vocabulary instruction has been well-studied, yet outcomes continue to be underwhelming. The specialized knowledge and expertise statisticians possess has the potential to enhance word learning research by applying sophisticated analyses not commonly used. Choosing vocabulary words for instruction can be a daunting task and is highly subjective. In an effort to aid in the selection process, researchers use a word selection framework that groups words into three tiers. Even …

Uncertainty Quantification In Deep And Statistical Learning With Applications In Bio-Medical Image Analysis, K. Ruwani M. Fernando

USF Tampa Graduate Theses and Dissertations

Deep Learning (DL) has achieved the state-of-the-art performance across a broad spectrum oftasks. From a statistical standpoint, deep neural networks can be construed as universal function approximators. Although statistical modeling and deep learning methods are well-established as independent areas of research, hybridization of the two paradigms via probabilistic deep networks is an emerging trend. Through development of novel analytical methods under the statistical and deep-learning framework, we address some of the major challenges encountered in the design of intelligent systems which include class imbalance learning, probability calibration, uncertainty quantification and high dimensionality. When modeling rare events, existing methodologies require re-sampling …