Browsing by Thesis Department "Statistics (MU)"
Now showing items 120 of 109

Adaptive optimal design with application to a two drug combination trial based on efficiencytoxicity response
(University of MissouriColumbia, 2009)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The first part of this dissertation develops an adaptive optimal design for dosefinding with combination therapies that accounts for both efficacy ... 
Adaptive optimal designs for dosefinding studies and an adaptive multivariate CUSUM control chart
(University of MissouriColumbia, 2013)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] There are many areas where optimal designs are applied to, for example, the development of a new drug, where a conventional dose finding study involves ... 
Alternative learning strategies for spatiotemporal processes of complex animal behavior
(University of MissouriColumbia, 2020)The estimation of spatiotemporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ... 
Average treatment effect evaluation with timetoevent data in randomized clinical trials and observational studies
(University of MissouriColumbia, 2023)[EMBARGOED UNTIL 5/1/2024] The average treatment effect (ATE) is defined as the difference in the expected outcome between individuals receiving the treatment and those not receiving it. As a measure of the impact of a ... 
A ballooned betalogistic model
(University of MissouriColumbia, 2015)The beta distribution is a simple and flexible model in which responses are naturally confined to the finite interval (0,1). Its parameters can be related to covariates such as dose and gender through a regression model. ... 
Bayes factor consistency in linear models when p grows with n
(University of MissouriColumbia, 2009)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] This dissertation examines consistency of Bayes factors in the model comparison problem for linear models. Common approaches to Bayesian analysis of ... 
Bayesian analysis for detecting differentially expressed genes from RNAseq data
(University of MissouriColumbia, 2014)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces hmmSeq, a modelbased hierarchical Bayesian technique for detecting differentially expressed genes from RNAseq data. Our ... 
Bayesian analysis of capturerecapture model and diagnostic test in clinical trials
(University of MissouriColumbia, 2014)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Capturerecapture models have been widely used to estimate the size of a target wildlife population. There are three major sources of variations that ... 
Bayesian analysis of fMRI data and RNASeq time course experiment data
(University of MissouriColumbia, 2015)The present dissertation contains two parts. In the first part, we develop a new Bayesian analysis of functional MRI data. We propose a novel triple gamma Hemodynamic Response Function (HRF) including the component to ... 
Bayesian analysis of multivariate stochastic volatility and dynamic models
(University of MissouriColumbia, 2006)We consider a multivariate regression model with time varying volatilities in the error term. The time varying volatility for each component of the error is of unknown nature, may be deterministic or stochastic. We propose ... 
Bayesian analysis of spatial and survival models with applications of computation techniques
(University of MissouriColumbia, 2012)This dissertation discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with rightcensored failure time. A conditional autoregressive (CAR) prior is ... 
A Bayesian approach to datadriven discovery of nonlinear dynamic equations
(University of MissouriColumbia, 2022)Dynamic equations parameterized by differential equations are used to represent a variety of realworld processes. The equations used to describe these processes are generally derived based on physical principles and a ... 
A Bayesian classification framework with label corrections
(University of MissouriColumbia, 2014)The use of unlabeled data is very important for regression and classification analysis in many cases. However, the data may have an extra layer of complexity with some wrongly labelled data points. The traditional ... 
Bayesian cusp regression and linear mixed model
(University of MissouriColumbia, 2022)First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piecewise continuous outcomes. Simulation and real data analysis show that the new method beats the ... 
Bayesian fMRI data analysis and Bayesian optimal design
(University of MissouriColumbia, 2012)The present dissertation consists of the work done on two projects. As part of the first project, we develop methodology for Bayesian hierarchical multisubject multiscale analysis of functional magnetic resonance imaging ... 
Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri
(University of MissouriColumbia, 2018)Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCRARC) maintains a statewide cancer surveillance system and participate in research in support ... 
Bayesian hierarchical models for estimating nest survival
(University of MissouriColumbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihoodbased logistic regression model ... 
Bayesian hierarchical models for estimating nest survival
(University of MissouriColumbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihoodbased logistic regression model ... 
Bayesian hierarchical models for the recognitionmemory experiments
(University of MissouriColumbia, 2008)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian hierarchical probit models are developed for analyzing the data from the recognitionmemory experiment in Psychology. Both informative priors ... 
Bayesian lasso for random intercept factor model
(University of MissouriColumbia, 2013)Structural Equation Models (SEM) are often used in psychological research. In many studies, determining the number of variables is di fficult because maximum likelihood estimates are empirically underidenti fied when more ...