Browsing by Thesis Advisor "Sun, Dongchu"
Now showing items 1-20 of 20
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Bayesian analysis of multivariate stochastic volatility and dynamic models
(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
(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 cusp regression and linear mixed model
(University of Missouri--Columbia, 2022)First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the ... -
Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri
(University of Missouri--Columbia, 2018)Data on cancer in the United States is collected through cancer registries. The Missouri Cancer Registry and Research Center (MCR-ARC) maintains a statewide cancer surveillance system and participate in research in support ... -
Bayesian hierarchical models for the recognition-memory experiments
(University of Missouri--Columbia, 2008)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian hierarchical probit models are developed for analyzing the data from the recognition-memory experiment in Psychology. Both informative priors ... -
Bayesian methods on selected topics
(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 semiparametric spatial and joint spatio-temporal modeling
(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 models and their application in estimating yield curves
(University of Missouri--Columbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] 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 ... -
Bayesian smoothing spline with dependency models
(University of Missouri--Columbia, 2021)The smoothing spline model is widely used for fitting a smooth curve because of its flexibility and smoothing properties. Our study is motivated by estimating the long-term trend of the U.S. unemployment level. In this ... -
Bayesian spatial models for adjusting nonresponse in small area estimation
(University of Missouri--Columbia, 2010)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There are two kinds of nonresponse: item nonresponse and unit nonresponse. Inferences made from respondents about the population of interest will be ... -
Data combining using mixtures of g-priors with application on county-level female breast cancer prevalence
(University of Missouri--Columbia, 2022)As more and more data are available, data synthesis has become an indispensable task for researchers. From a Bayesian perspective, this dissertation includes three related projects and aims at quantifying the benefits of ... -
Estimating population size with objective Bayesian methods
(University of Missouri--Columbia, 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 Missouri--Columbia, 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. ... -
Marginally modeling misaligned regions and handling masked failure causes with imprecision
(University of Missouri--Columbia, 2012)For many datasets, multiple variables measured on (possibly differing) areal units are available. We wish to simultaneously model both 1) the spatial relations within each variable, and 2) the relations between variables. ... -
Objective Bayesian analysis of the 2 x 2 contingency table and the negative binomial distribution
(University of Missouri--Columbia, 2018)In Bayesian analysis, the “objective” Bayesian approach seeks to select a prior distribution not by using (often subjective) scientific belief or by mathematical convenience, but rather by deriving it under a pre-specified ... -
Objective Bayesian inference for stress-strength models and Bayesian ANOVA
(University of Missouri--Columbia, 2012)First of all, for estimating the reliabilities in Weibull stress-strength models, some matching priors are derived based on a modi ed pro le likelihood. Simulation studies show that these matching priors perform well with ... -
Partially informative normal and partial spline models
(University of Missouri--Columbia, 2015)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] There is a well-known Bayesian interpretation of function estimation by spline smoothing using a limit of proper normal priors. This limiting prior ... -
Some topics in multi-regional clinical trials and meta-analysis using Bayesian models
(University of Missouri--Columbia, 2017)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The dissertation consists of two distinct research topics. One is about sample size determination in Multi-Regional Clinical Trials (MRCTs), the other ... -
Topics in imbalanced data classification : AdaBoost and Bayesian relevance vector machine
(University of Missouri--Columbia, 2020)This research has three parts addressing classification, especially the imbalanced data problem, which is one of the most popular and essential issues in the domain of classification. The first part is to study the Adaptive ... -
Topics in objective bayesian methodology and spatio-temporal models
(University of Missouri--Columbia, 2008)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Three distinct but related topics contribute my work in objective Bayesian methodology and spatio-temporal models. This dissertation starts with the ...