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Now showing items 61-80 of 112
Equivalence test of high dimensional microarray data
(University of Missouri--Columbia, 2014)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The Booth lab at the University of Missouri has selectively-bred Wistar rats for low (LVR) and high (HVR) voluntary running behavior as a model for ...
A hierarchical Bayesian mixture approach for modeling reflectivity fields with application to Nowcasting
(University of Missouri--Columbia, 2009)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] We study a hierarchical Bayesian framework for finite mixtures of distributions. We first consider a Dirichlet mixture of normal components and utilize ...
Optimal designs for response functions with a downturn
(University of Missouri--Columbia, 2010)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] In many toxicological assays, interactions between primary and secondary effects may cause a downturn in mean responses at high doses. In this situation, ...
Semiparametric transformation models for panel count data
(University of Missouri--Columbia, 2011)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Panel count data arise in event history studies. It may not be feasible to monitor subjects continuously and recurrent events can be observed only at ...
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 ...
Two stage adaptive optimal design with applications to dose-finding clinical trials
(University of Missouri--Columbia, 2013)
In adaptive optimal designs, each stage uses an estimate of the locally optimal design derived using cumulative data from all prior stages. This dependency on prior stages a ffects the properties of maximum likelihood ...
Random set models for growth with applications to nowcasting
(University of Missouri--Columbia, 2013)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] We develop models to capture the growth or evolution of objects over time as well as provide forecasts to describe the object in future states utilizing ...
Flexible Bayesian Hierarchical Models for Discrete-Valued Spatio-Temporal Data
(University of Missouri--Columbia, 2014)
Statistical analysis of multivariate interval-censored failure time data
(University of Missouri--Columbia, 2006)
Interval-censored 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 ...
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 ...
Statistical analysis of failure time data with missing information
(University of Missouri--Columbia, 2009)
Failure time data arise in many fields and can involve different types of censoring structures and missing information. We consider three cases: right-censored data with missing censoring indicators, clustered current ...
A mixed model for variance of successive difference of stationary time series : modeling temporal instability in intensive longitudinal data
(University of Missouri--Columbia, 2008)
Temporal instability of a stochastic process has been of interest in many areas of behavioral and social science. Recent development in data collection techniques in behavioral and health sciences, such as Ecological ...
Semiparametric analysis of multivariate longitudinal data
(University of Missouri--Columbia, 2008)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Longitudinal studies are conducted widely in fields such as agriculture and life sciences, business and industry, demography and other social sciences, ...
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 spatial analysis with application to the Missouri Ozark Forest ecosystem project
(University of Missouri--Columbia, 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 ...
Spatially adaptive priors for regression and spatial modeling
(University of Missouri--Columbia, 2008)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] The smoothing splines and penalized regression splines (P-splines) are popular nonparametric regression methods for curve fitting problems, and the ...
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 ...
Nonparametric analysis of interval-censored failure time data
(University of Missouri--Columbia, 2009)
This thesis considers the problem of treatment comparisons when only interval-censored failure time data are available. This type of data occurs frequently in clinical trials and other follow-up studies. We study several ...
Nonparametric and semiparametric methods for interval-censored failure time data
(University of Missouri--Columbia, 2006)
Interval-censored failure time data commonly arise in follow-up studies such as clinical trials and epidemiology studies. By interval-censored data, we mean that the failure time of interest is not completely observed. ...
Optimal designs for dose-finding in contingent response models
(University of Missouri--Columbia, 2004)
We study D- and c-optimal designs for dose-finding with opposing failure functions. In particular, we study the contingent response models of Li, Durham and Flournoy (1995). In the contingent response model, there are two ...