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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. ...
Bayesian spatial data analysis with application to the Missouri Ozark forest ecosystem project
(University of Missouri--Columbia, 2006)
The first part studies the problem of estimating the covariance matrix in a star-shaped model with missing data. By introducing a class of priors based on a type of Cholesky decomposition of the precision matrix, we then ...
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 ...
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 ...
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 ...
Hierarchical spatio-temporal models for ecological processes
(University of Missouri--Columbia, 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 ...
Hierarchical spatio-temporal models for environmental processes
(University of Missouri--Columbia, 2007)
The processes governing environmental systems are often complex, involving different interacting scales of variability in space and time. The complexities and often high dimensionality of such spatio-temporal processes can ...
Semiparametric analysis of panel count data
(University of Missouri--Columbia, 2007)
Panel count data often arise in long term studies that concern occurrence rates of certain recurrent events. In such studies, each subject is observed only at finite discrete time points instead of continuously, and only ...
Statistical analysis of multivariate interval-censored failure time data
(University of Missouri--Columbia, 2007)
A voluminous literature on right-censored failure time data has been developed in the past 30 years. Due to advances in biomedical research, interval censoring has become increasingly common in medical follow-up studies. ...
Variable selection for interval-censored failure time data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Variable selection is a commonly asked question and various traditional variable selection methods have been developed, including forward, backward and ...
Bayesian non-parametric methods for benefit-risk assessment and massive multiple-domain data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] The development of systematic and structured approaches to assess benefit-risk of medical products is a major challenge for regulatory decision makers. ...
Decision theory and sampling algorithms for spatial and spatio-temporal point processes
(University of Missouri--Columbia, 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 ...
Point processes on the complex plane with applications
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A point process is a random collection of points from a certain space, and point process models are widely used in areas dealing with spatial data. ...
Modeling gibbs point processes through basic function decompositions
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] We consider non-homogeneous pairwise interaction point process models, where the global and local effect functions are modeled using basis function ...
Alternative learning strategies for spatio-temporal processes of complex animal behavior
(University of Missouri--Columbia, 2020)
The estimation of spatio-temporal dynamics of animal behavior processes is complicated by nonlinear interactions. Alternative learning methods such as machine learning, deep learning, and reinforcement learning have proven ...
Regression analysis of interval-censored failure time data with non proportional hazards models
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Interval-censored failure time data arises when the failure time of interest is known only to lie within an interval or window instead of being observed ...
Semiparametric analysis of complex longitudinal data
(University of Missouri--Columbia, 2020)
Event history data consist of the longitudinal records of event occurrence times. Recurrent event data and panel count data are two common types of event history data that occur in many areas, such as medical studies and ...
Nonlocal priors for Bayesian variable selection in generalized linear models and generalized linear mixed models and their applications in biology data
(University of Missouri--Columbia, 2016)
A crucial problem in building a generalized linear model (GLM) or a generalized linear mixed model (GLMM) is to identify which subset of predictors should be included into the model. Hence, the main thrust of this dissertation ...
Semiparametric methods for regression analysis of panel count data and mixed panel count data
(University of Missouri--Columbia, 2017)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Recurrent event data and panel count data are two common types of data that have been studied extensively in event history studies in literature. By ...