Physical Sciences and Mathematics Commons^™

Bayesian Methods For Graphical Models With Neighborhood Selection., Sagnik Bhadury

Electronic Theses and Dissertations

Graphical models determine associations between variables through the notion of conditional independence. Gaussian graphical models are a widely used class of such models, where the relationships are formalized by non-null entries of the precision matrix. However, in high-dimensional cases, covariance estimates are typically unstable. Moreover, it is natural to expect only a few significant associations to be present in many realistic applications. This necessitates the injection of sparsity techniques into the estimation method. Classical frequentist methods, like GLASSO, use penalization techniques for this purpose. Fully Bayesian methods, on the contrary, are slow because they require iteratively sampling over a quadratic …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Propensity Score Methods : A Simulation And Case Study Involving Breast Cancer Patients., John Craycroft

Electronic Theses and Dissertations

Observational data presents unique challenges for analysis that are not encountered with experimental data resulting from carefully designed randomized controlled trials. Selection bias and unbalanced treatment assignments can obscure estimations of treatment effects, making the process of causal inference from observational data highly problematic. In 1983, Paul Rosenbaum and Donald Rubin formalized an approach for analyzing observational data that adjusts treatment effect estimates for the set of non-treatment variables that are measured at baseline. The propensity score is the conditional probability of assignment to a treatment group given the covariates. Using this score, one may balance the covariates across treatment …

Meta-Analysis Of Lapatinib Plus Capecitabine Versus Capecitabine In The Treatment Of Her2 Positive Breast Cancer, Lynda Smith

Culminating Projects in Applied Statistics

BACKGROUND:

Breast cancer is the most common type of cancer in women despite advances in research and detection methods. Approximately 25 to 30 percent of newly diagnosed cases of breast cancer will overexpress HER2, human epidermal growth factor receptor 2, and are at a greater risk for disease progression and poorer clinical outcomes. The traditional treatment is associated with irreversible cardiac dysfunction. An alternative treatment involving lapatinib plus capecitabine has been reported in some randomized controlled clinical trials comparing treatment outcomes. To quantify the effectiveness of lapatinib plus capecitabine combination therapy versus capecitabine monotherapy in treating metastatic breast cancer, a …

An Analysis Of Breast Cancer Metastasis, Jennifer Lee Gildner

Statistics

The main objective of this paper is to evaluate possible socio-economic status, clinical, and treatment associations with the occurrence of distant metastasis in Stage I – III breast cancer patients. After analysis in a logistic regression model, four variables were found to be significant with occurrence of distant metastases. These variables were: education, disease group (Triple-negative, Her2Neu-positive and Luminal A), stage at diagnosis, and concordance to chemotherapy based on the NCCN guidelines. Patients without a college degree were found to be more likely to develop distant metastasis than those with a college degree (OR = 2.46 95% CI 1.44 – …

Statistical Models For Environmental And Health Sciences, Yong Xu

USF Tampa Graduate Theses and Dissertations

Statistical analysis and modeling are useful for understanding the behavior of different phenomena. In this study we will focus on two areas of applications: Global warming and cancer research. Global Warming is one of the major environmental challenge people face nowadays and cancer is one of the major health problem that people need to solve.

For Global Warming, we are interest to do research on two major contributable variables: Carbon dioxide (CO2) and atmosphere temperature. We will model carbon dioxide in the atmosphere data with a system of differential equations. We will develop a differential equation for each of six …

Parametric And Bayesian Modeling Of Reliability And Survival Analysis, Carlos A. Molinares

USF Tampa Graduate Theses and Dissertations

The objective of this study is to compare Bayesian and parametric approaches to determine the best for estimating reliability in complex systems. Determining reliability is particularly important in business and medical contexts. As expected, the Bayesian method showed the best results in assessing the reliability of systems.

In the first study, the Bayesian reliability function under the Higgins-Tsokos loss function using Jeffreys as its prior performs similarly as when the Bayesian reliability function is based on the squared-error loss. In addition, the Higgins-Tsokos loss function was found to be as robust as the squared-error loss function and slightly more efficient. …