Browsing Theses and Dissertations (MU) by Thesis Department "Statistics"
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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 ... 
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 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 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 spatial data analysis with application to the Missouri Ozark forest ecosystem project
(University of MissouriColumbia, 2006)The first part studies the problem of estimating the covariance matrix in a starshaped model with missing data. By introducing a class of priors based on a type of Cholesky decomposition of the precision matrix, we then ... 
Bayesian spatial models for adjusting nonresponse in small area estimation
(University of MissouriColumbia, 2010)There are two kinds of nonresponse: item nonresponse and unit nonresponse. Inferences made from respondents about the population of interest will be subject to nonresponse bias in both situations. When the population of ... 
Bayesian variable selection in parametric and semiparametric high dimensional survival analysis
(University of MissouriColumbia, 2011)In this dissertation, we propose several Bayesian variable selection schemes for Bayesian parametric and semiparametric survival models for rightcensored survival data. In the rst chapter we introduce a special shrinkage ... 
Design and analysis of a new bounded loglinear regression model
(University of MissouriColumbia, 2013)This dissertation introduces a new regression model in which the response variable is bounded by two unknown parameters. A special case is a bounded alternative to the four parameter logistic model which is also called the ... 
Empirical likelihood approach estimation of structural equation models
(University of MissouriColumbia, 2007)This thesis provides a preliminary investigation of empirical likelihood approach estimation of structural equation models. An auxiliary variable approach built on general estimating equation methods in the EL settings is ... 
Estimates of school productivity and implications for policy
(University of MissouriColumbia, 2007)School productivity was not perfectly estimated because of the sampling error and the measurement error. The traditional Ordinary Least Square (OLS) leaves the estimation of school productivity questionable. Moreover, ... 
Estimating population size with objective Bayesian methods
(University of MissouriColumbia, 2012)Bayesian inference of discrete parameter, including population size, is sensitive to the choice of priors. In this dissertation I will develop objective priors for several population size parameters appeared in different ... 
THE FORMAL DEFINITION OF REFERENCE PRIORS UNDER A GENERAL CLASS OF DIVERGENCE
([University of MissouriColumbia], 2014)Bayesian analysis is widely used recently in both theory and application of statistics. The choice of priors plays a key role in any Bayesian analysis. There are two types of priors: subjective priors and objective priors. ... 
A hierarchical Bayesian mixture approach for modeling reflectivity fields with application to Nowcasting
(University of MissouriColumbia, 2009)We study a hierarchical Bayesian framework for finite mixtures of distributions. We first consider a Dirichlet mixture of normal components and utilize it to model spatial fields that arise as pixelated images of intensities. ... 
Hierarchical modeling of nonlinear multivariate spatiotemporal dynamical systems in the presence of uncertainty
(University of MissouriColumbia, 2012)Dynamic spatiotemporal models are statistical models that specify the joint distribution of a spatiotemporal process as the product of a series of conditional models whereby the current value of the process is conditioned ... 
Hierarchical nonlinear, multivariate, and spatiallydependent timefrequency functional methods
(University of MissouriColumbia, 2013)Notions of time and frequency are important constituents of most scientific inquiries, providing complimentary information. In an era of “big data,” methodology for analyzing functional and/or image data is increasingly ...