Browsing by Thesis Department "Statistics (MU)"
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Adaptive optimal design with application to a two drug combination trial based on efficiencytoxicity response
(University of MissouriColumbia, 2009)The first part of this dissertation develops an adaptive optimal design for dosefinding with combination therapies that accounts for both efficacy and toxicity. The bivariate probit model is used as a working model for ... 
Adaptive optimal designs for dosefinding studies and an adaptive multivariate CUSUM control chart
(University of MissouriColumbia, 2013)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 learning about the doseresponse curve in order to bring forward right ... 
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)This dissertation examines consistency of Bayes factors in the model comparison problem for linear models. Common approaches to Bayesian analysis of linear models use Zellner's gprior, a partially conjugate normal prior ... 
Bayesian analysis for detecting differentially expressed genes from RNAseq data
(University of MissouriColumbia, 2014)This dissertation introduces hmmSeq, a modelbased hierarchical Bayesian technique for detecting differentially expressed genes from RNAseq data. Our novel hmmSeq methodology uses hidden Markov models to account for ... 
Bayesian analysis of capturerecapture model and diagnostic test in clinical trials
(University of MissouriColumbia, 2014)Capturerecapture models have been widely used to estimate the size of a target wildlife population. There are three major sources of variations that can affect capture probabilities: time (i.e., capture probabilities vary ... 
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 ... 
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 models for estimating nest survival
(University of MissouriColumbia, 2015)Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihoodbased logistic regression model was evaluated in the first part of the dissertation. In this part, we ... 
Bayesian hierarchical models for estimating nest survival
(University of MissouriColumbia, 2015)Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihoodbased logistic regression model was evaluated in the first part of the dissertation. In this part, we ... 
Bayesian hierarchical models for the recognitionmemory experiments
(University of MissouriColumbia, 2008)Bayesian hierarchical probit models are developed for analyzing the data from the recognitionmemory experiment in Psychology. Both informative priors and noninformative priors are investigated. For the 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 ... 
Bayesian methods on selected topics
(University of MissouriColumbia, 2012)Bayesian methods are widely adopted nowadays in statistical analysis. It is especially useful for the statistical inference of complex models or hierarchical models, for which the frequentist methods are usually difficult ... 
Bayesian nonlinear methods for survival analysis and structural equation models
([University of MissouriColumbia], 2014)High dimensional data are more common nowadays, because the collection of such data becomes larger and more complex due to the technology advance of the computer science, biology, etc. The analysis of high dimensional data ... 
Bayesian semiparametric spatial and joint spatiotemporal modeling
(University of MissouriColumbia, 2006)Over the past decades a great deal of effort has been expended in the collection and compilation of high quality data on cancer incidence and mortality in the United States. These data have largely been used in the creation ... 
Bayesian smoothing spline analysis of variance models
(University of MissouriColumbia, 2009)Based on the pioneering work by Wahba (1990) in smoothing splines for nonparametric regression, Gu (2002) decomposed the regression function based on a tensor sum decomposition of inner product spaces into orthogonal ... 
Bayesian smoothing spline models and their application in estimating yield curves
(University of MissouriColumbia, 2015)The term structure of interest rates, also called the yield curve, is the series of interest rates ordered by term to maturity at a given time. The smoothing spline as a nonparametric regression method has been used widely ... 
Bayesian spatial analysis with application to the Missouri Ozark Forest ecosystem project
(University of MissouriColumbia, 2008)Bayesian hierarchical framework brings more flexibility by accounting for variation from different levels and improves the estimation of parameters as well as the prediction. When there are so many zeros in the data that ...