Physical Sciences and Mathematics Commons^™

Multiscale Modelling Of Brain Networks And The Analysis Of Dynamic Processes In Neurodegenerative Disorders, Hina Shaheen

Theses and Dissertations (Comprehensive)

The complex nature of the human brain, with its intricate organic structure and multiscale spatio-temporal characteristics ranging from synapses to the entire brain, presents a major obstacle in brain modelling. Capturing this complexity poses a significant challenge for researchers. The complex interplay of coupled multiphysics and biochemical activities within this intricate system shapes the brain's capacity, functioning within a structure-function relationship that necessitates a specific mathematical framework. Advanced mathematical modelling approaches that incorporate the coupling of brain networks and the analysis of dynamic processes are essential for advancing therapeutic strategies aimed at treating neurodegenerative diseases (NDDs), which afflict millions of …

Bayesian Hierarchical Temporal Modeling And Targeted Learning With Application To Reproductive Health, Herbert P. Susmann

Doctoral Dissertations

The international community via the United Nations Sustainable Development Goals has set the target of universal access to reproductive health-care services, including family planning, by 2030. Progress towards reaching this goal is assessed by tracking appropriate demographic and health indicators at national and subnational levels. This task is challenging, however, in populations where relevant data are limited or of low quality. Statistical models are then needed to estimate and project demographic and health indicators in populations based on the available data. Our first contribution, in Chapter 1, is to unify many existing demographic and health indicator models by proposing an …

Bayesian Multivariate Joint Modeling For Skewed-Longitudinal And Time-To-Event Data, Lan Xu

USF Tampa Graduate Theses and Dissertations

In epidemiologic and clinical studies, a relatively large number of biomarkers are repeatedly measured in patients over time, often associated with data on epidemiologic and clinical interest events. So, much attention is focused on developing the specific patterns of the longitudinal measurements, and the associations between those patterns and the time to a certain event, such as heart attack, diagnose of disease, time to transplantation, or death. In the last two decades, the research into joint modeling of longitudinal and time-to-event data has received a tremendous amount of attention.

Numerous researchers have proposed joint modeling approaches for a single longitudinal …

Point Process Modelling Of Objects In The Star Formation Complexes Of The M33 Galaxy, Dayi Li

Electronic Thesis and Dissertation Repository

In this thesis, Gibbs point process (GPP) models are constructed to study the spatial distribution of objects in the star formation complexes of the M33 galaxy. The GPP models circumvent the limitations of the two-point correlation function employed in the current astronomy literature by naturally accounting for the inhomogeneous distribution of these objects. The spatial distribution of these objects serves as a sensitive probe in understanding the star formation process, which is crucial in understanding the formation of galaxies and the Universe. The objects under study include the CO filament structure, giant molecular clouds (GMCs) and young stellar cluster candidates …

Exploring The Estimability Of Mark-Recapture Models With Individual, Time-Varying Covariates Using The Scaled Logit Link Function, Jiaqi Mu

Electronic Thesis and Dissertation Repository

Mark-recapture studies are often used to estimate the survival of individuals in a population and identify factors that affect survival in order to understand how the population might be affected by changing conditions. Factors that vary between individuals and over time, like body mass, present a challenge because they can only be observed when an individual is captured. Several models have been proposed to deal with the missing-covariate problem and commonly impose a logit link function which implies that the survival probability varies between 0 and 1. In this thesis I explore the estimability of four possible models when survival …

Bayesian Analysis For The Intraclass Model And For The Quantile Semiparametric Mixed-Effects Double Regression Models, Duo Zhang

Dissertations, Master's Theses and Master's Reports

This dissertation consists of three distinct but related research projects. The first two projects focus on objective Bayesian hypothesis testing and estimation for the intraclass correlation coefficient in linear models. The third project deals with Bayesian quantile inference for the semiparametric mixed-effects double regression models. In the first project, we derive the Bayes factors based on the divergence-based priors for testing the intraclass correlation coefficient (ICC). The hypothesis testing of the ICC is used to test the uncorrelatedness in multilevel modeling, and it has not well been studied from an objective Bayesian perspective. Simulation results show that the two sorts …

Smart Classifiers And Bayesian Inference For Evaluating River Sensitivity To Natural And Human Disturbances: A Data Science Approach, Kristen Underwood

Graduate College Dissertations and Theses

Excessive rates of channel adjustment and riverine sediment export represent societal challenges; impacts include: degraded water quality and ecological integrity, erosion hazards to infrastructure, and compromised public safety. The nonlinear nature of sediment erosion and deposition within a watershed and the variable patterns in riverine sediment export over a defined timeframe of interest are governed by many interrelated factors, including geology, climate and hydrology, vegetation, and land use. Human disturbances to the landscape and river networks have further altered these patterns of water and sediment routing.

An enhanced understanding of river sediment sources and dynamics is important for stakeholders, and …

Bayesian Inference On Quantile Regression-Based Mixed-Effects Joint Models For Longitudinal-Survival Data From Aids Studies, Hanze Zhang

USF Tampa Graduate Theses and Dissertations

In HIV/AIDS studies, viral load (the number of copies of HIV-1 RNA) and CD4 cell counts are important biomarkers of the severity of viral infection, disease progression, and treatment evaluation. Recently, joint models, which have the capability on the bias reduction and estimates' efficiency improvement, have been developed to assess the longitudinal process, survival process, and the relationship between them simultaneously. However, the majority of the joint models are based on mean regression, which concentrates only on the mean effect of outcome variable conditional on certain covariates. In fact, in HIV/AIDS research, the mean effect may not always be of …

An Enhanced Bridge Weigh-In-Motion Methodology And A Bayesian Framework For Predicting Extreme Traffic Load Effects Of Bridges, Yang Yu

LSU Doctoral Dissertations

In the past few decades, the rapid growth of traffic volume and weight, and the aging of transportation infrastructures have raised serious concerns over transportation safety. Under these circumstances, vehicle overweight enforcement and bridge condition assessment through structural health monitoring (SHM) have become critical to the protection of the safety of the public and transportation infrastructures. The main objectives of this dissertation are to: (1) develop an enhanced bridge weigh-in-motion (BWIM) methodology that can be integrated into the SHM system for overweight enforcement and monitoring traffic loading; (2) present a Bayesian framework to predict the extreme traffic load effects (LEs) …

Inference From Network Data In Hard-To-Reach Populations, Isabelle Beaudry

Doctoral Dissertations

The objective of this thesis is to develop methods to make inference about the prevalence of an outcome of interest in hard-to-reach populations. The proposed methods address issues specific to the survey strategies employed to access those populations. One of the common sampling methodology used in this context is respondent-driven sampling (RDS). Under RDS, the network connecting members of the target population is used to uncover the hidden members. Specialized techniques are then used to make inference from the data collected in this fashion. Our first objective is to correct traditional RDS prevalence estimators and their associated uncertainty estimators for …

Monte Carlo Methods In Bayesian Inference: Theory, Methods And Applications, Huarui Zhang

Graduate Theses and Dissertations

Monte Carlo methods are becoming more and more popular in statistics due to the fast development of efficient computing technologies. One of the major beneficiaries of this advent is the field of Bayesian inference. The aim of this thesis is two-fold: (i) to explain the theory justifying the validity of the simulation-based schemes in a Bayesian setting (why they should work) and (ii) to apply them in several different types of data analysis that a statistician has to routinely encounter. In Chapter 1, I introduce key concepts in Bayesian statistics. Then we discuss Monte Carlo Simulation methods in detail. Our …

Acceptance-Rejection Sampling With Hierarchical Models, Christian A. Ayala

CMC Senior Theses

Hierarchical models provide a flexible way of modeling complex behavior. However, the complicated interdependencies among the parameters in the hierarchy make training such models difficult. MCMC methods have been widely used for this purpose, but can often only approximate the necessary distributions. Acceptance-rejection sampling allows for perfect simulation from these often unnormalized distributions by drawing from another distribution over the same support. The efficacy of acceptance-rejection sampling is explored through application to a small dataset which has been widely used for evaluating different methods for inference on hierarchical models. A particular algorithm is developed to draw variates from the posterior …

Statistical Modeling And Prediction Of Hiv/Aids Prognosis: Bayesian Analyses Of Nonlinear Dynamic Mixtures, Xiaosun Lu

USF Tampa Graduate Theses and Dissertations

Statistical analyses and modeling have contributed greatly to our understanding of the pathogenesis of HIV-1 infection; they also provide guidance for the treatment of AIDS patients and evaluation of antiretroviral (ARV) therapies. Various statistical methods, nonlinear mixed-effects models in particular, have been applied to model the CD4 and viral load trajectories. A common assumption in these methods is all patients come from a homogeneous population following one mean trajectories. This assumption unfortunately obscures important characteristic difference between subgroups of patients whose response to treatment and whose disease trajectories are biologically different. It also may lack the robustness against population heterogeneity …