• Adaptive optimal design with application to a two drug combination trial based on efficiency-toxicity response 

    Yao, Ping, Ph. D. (University of Missouri--Columbia, 2009)
    The first part of this dissertation develops an adaptive optimal design for dose-finding 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 dose-finding studies and an adaptive multivariate CUSUM control chart 

    Wang, Tianhua (University of Missouri--Columbia, 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 dose-response curve in order to bring forward right ...
  • A ballooned beta-logistic model 

    Yi, Min (University of Missouri--Columbia, 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 

    Guo, Ruixin, 1983- (University of Missouri--Columbia, 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 g-prior, a partially conjugate normal prior ...
  • Bayesian analysis for detecting differentially expressed genes from RNA-seq data 

    Cui, Shiqi (University of Missouri--Columbia, 2014)
    This dissertation introduces hmmSeq, a model-based hierarchical Bayesian technique for detecting differentially expressed genes from RNA-seq data. Our novel hmmSeq methodology uses hidden Markov models to account for ...
  • Bayesian analysis of capture-recapture model and diagnostic test in clinical trials 

    Zheng, Dan (University of Missouri--Columbia, 2014)
    Capture-recapture 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 RNA-Seq Time Course experiment data 

    Cheng, Yuan (University of Missouri--Columbia, 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 

    Loddo, Antonello, 1976- (University of Missouri--Columbia, 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 

    Liu, Yajun (University of Missouri--Columbia, 2012)
    This dissertation discusses the methodologies of applying Bayesian hierarchical models to different data with geographical characteristics or with right-censored failure time. A conditional autoregressive (CAR) prior is ...
  • Bayesian fMRI data analysis and Bayesian optimal design 

    Sanyal, Nilotpal (University of Missouri--Columbia, 2012)
    The present dissertation consists of the work done on two projects. As part of the first project, we develop methodology for Bayesian hierarchical multi-subject multiscale analysis of functional magnetic resonance imaging ...
  • Bayesian hierarchical models for estimating nest survival 

    Yang, Yiqun (University of Missouri--Columbia, 2015)
    Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihood-based logistic regression model was evaluated in the first part of the dissertation. In this part, we ...
  • Bayesian hierarchical models for estimating nest survival 

    Yang, Yiqun (University of Missouri--Columbia, 2015)
    Nest survival rate is a critical value in avian study to evaluate the landbirds populations. The widely used likelihood-based logistic regression model was evaluated in the first part of the dissertation. In this part, we ...
  • Bayesian hierarchical models for the recognition-memory experiments 

    Lin, Xiaoyan, 1978- (University of Missouri--Columbia, 2008)
    Bayesian hierarchical probit models are developed for analyzing the data from the recognition-memory experiment in Psychology. Both informative priors and non-informative priors are investigated. For the informative priors, ...
  • Bayesian lasso for random intercept factor model 

    Wang, Ting (University of Missouri--Columbia, 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 under-identi fied when more ...
  • Bayesian methods on selected topics 

    Liang, Ye (University of Missouri--Columbia, 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 non-linear methods for survival analysis and structural equation models 

    Wang, Zhenyu (Statistician) ([University of Missouri--Columbia], 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 spatio-temporal modeling 

    White, Gentry, 1972- (University of Missouri--Columbia, 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 

    Cheng, Chin-I (University of Missouri--Columbia, 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 

    Tong, Xiaojun (University of Missouri--Columbia, 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 

    Zhang, Jing, 1981- (University of Missouri--Columbia, 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 ...