Browsing Department of Statistics (MU) by Subject "Bayesian statistical decision theory"
Now showing items 119 of 19

Accounting for Uncertainty in Ecological Analysis: The Strengths and Limitations of Hierarchical Statistical Modeling
(Ecological Society of America, 200904)Analyses of ecological data should account for the uncertainty in the process(es) that generated the data. However, accounting for these uncertainties is a difficult task, since ecology is known for its complexity. ... 
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 ... 
A Bayesian Approach to Estimating the Long Memory Parameter
(Bayesian Analysis, 2009)We develop a Bayesian procedure for analyzing stationary longrange dependent processes. Specifically, we consider the fractional exponential model (FEXP) to estimate the memory parameter of a stationary longmemory Gaussian ... 
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 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 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 ... 
Gene ExpressionBased Glioma Classification Using Hierarchical Bayesian Vector Machines
(Indian Statistical Institute, 2007)This paper considers several Bayesian classification methods for the analysis of the glioma cancer with microarray data based on reproducing kernel Hilbert space under the multiclass setup. We consider the multinomial ... 
Hierarchical Bayesian Approach to Boundary Value Problems with Stochastic Boundary Conditions
(American Meteorological Society, 2003)Boundary value problems are ubiquitous in the atmospheric and ocean sciences. Typical settings include bounded, partially bounded, global and limited area domains, discretized for applications of numerical models of the ... 
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 Bayesian Models for Predicting The Spread of Ecological Processes
(Ecological Society of America, 2003)There is increasing interest in predicting ecological processes. Methods to accomplish such predictions must account for uncertainties in observation, sampling, models, and parameters. Statistical methods for spatiotemporal ... 
Hierarchical physicalstatistical forecasting in the atmospheric sciences
(University of MissouriColumbia, 2009)A class of hierarchical Bayesian models is introduced for PhysicalStatistical forecasting purposes in the Atmospheric Sciences. The first project describes a methodological approach to implement a stochastic trigger ... 
A KernelBased Spectral Model for NonGaussian SpatioTemporal Processes
(Statistical Modelling, 2002)Spatiotemporal processes can often be written as hierarchical statespace processes. In situations with complicated dynamics such as wave propagation, it is difficult to parameterize state transition functions for ... 
Population Influences on Tornado Reports in the United States
(Weather and Forecasting, 200504)The number of tornadoes reported in the United States is believed to be less than the actual incidence of tornadoes, especially prior to the 1990s, because tornadoes may be undetectable by human witnesses in sparsely ... 
Predicting the Spatial Distribution of Ground Flora on Large Domains Using a Hierarchical Bayesian Model
(Landscape Ecology, 2003)Accomodation of important sources of uncertainty in ecological models is essential to realistically predicting ecological processes. The purpose of this project is to develop a robust methodology for modeling natural ... 
SpatioTemporal Hierarchical Bayesian Modeling: Tropical Ocean Surface Winds
(American Statistical Association, 2001)Spatiotemporal processes are ubiquitous in the environmental and physical sciences. This is certainly true of atmospheric and oceanic processes, which typically exhibit many different scales of spatial and temporal ... 
Topics in objective bayesian methodology and spatiotemporal models
(University of MissouriColumbia, 2008)Three distinct but related topics contribute my work in objective Bayesian methodology and spatiotemporal models. This dissertation starts with the study of a class of objective priors on normal means and variance in a ...