Browsing 2006 MU dissertations  Freely available online by Thesis Department "Statistics"
Now showing items 16 of 6

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 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 ... 
Hierarchical spatiotemporal models for ecological processes
(University of MissouriColumbia, 2006)Ecosystems are composed of phenomena that propagate in time and space. Often, ecological processes underlying such phenomena are studied separably in various subdisciplines, while larger scale, interlinking mechanisms are ... 
Nonparametric and semiparametric methods for intervalcensored failure time data
(University of MissouriColumbia, 2006)Intervalcensored failure time data commonly arise in followup studies such as clinical trials and epidemiology studies. By intervalcensored data, we mean that the failure time of interest is not completely observed. ... 
Statistical analysis of multivariate intervalcensored failure time data
(University of MissouriColumbia, 2006)Intervalcensored failure time data commonly arise in clinical trials and medical studies. In such studies, the failure time of interest is often not exactly observed, but known to fall within some interval. For multivariate ...