Browsing Graduate School  MU Theses and Dissertations (MU) by Thesis Department "Statistics (MU)"
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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 model averaging for mathematics achievement growth rate trends
(University of MissouriColumbia, 2022)In this study, we investigated the use of Bayesian model averaging (BMA) for latent growth curve models. We used the Trends in International Mathematics and Science Study (TIMSS) to predict growth rates in 8thgrade students' ... 
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 nonparametric methods for benefitrisk assessment and massive multipledomain data
(University of MissouriColumbia, 2019)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The development of systematic and structured approaches to assess benefitrisk of medical products is a major challenge for regulatory decision makers. ... 
Bayesian partition model for identifying hypo and hyper methylation
(University of MissouriColumbia, 2017)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] This dissertation introduces MethyBayes, a full Bayesian partition model for identifying hypo and hypermethylated loci. The main interest of study on ... 
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)[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 MissouriColumbia, 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 longterm trend of the U.S. unemployment level. In this ... 
Bayesian spatial analysis with application to the Missouri Ozark Forest ecosystem project
(University of MissouriColumbia, 2008)[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian hierarchical framework brings more flexibility by accounting for variation from different levels and improves the estimation of parameters ... 
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)[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 ... 
Bayesian unitlevel modeling of nonGaussian survey data under informative sampling with application to small area estimation
(University of MissouriColumbia, 2021)Unitlevel models are an alternative to the traditional arealevel models used in small area estimation, characterized by the direct modeling of survey responses rather than aggregated direct estimates. These unitlevel ... 
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 ... 
A class of weakly informative prior on multinomial logistic regression with separated data
(University of MissouriColumbia, 2021)Complete separation in logistic regression, sometimes referred to as perfect prediction, occurs when the outcome variable completely separates predictor variables. The likelihood function is monotonically increasing on the ... 
Data combining using mixtures of gpriors with application on countylevel female breast cancer prevalence
(University of MissouriColumbia, 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 ... 
Decision theory and sampling algorithms for spatial and spatiotemporal point processes
(University of MissouriColumbia, 2019)In this work, we first present a flexible hierarchical Bayesian model and develop a comprehensive Bayesian decision theoretic framework for point process theory. Then, we provide a comparative study of the approximate ... 
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 ... 
Dynamic analysis of complex panel count data
(University of MissouriColumbia, 2021)Panel count data occur in many fields including clinical, demographical and industrial studies and an extensive literature has been established for their regression analysis. However, most of the existing methods apply ...