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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. ...
Statistical analysis of clustered or multivariate interval-censored failure time data
(University of Missouri--Columbia, 2018)
of the presented methodology, an extensive simulation study is performed and suggests that the method works well in practical situations. Finally, the proposed approach is applied to a tumorigenicity experiment. Several directions for future research are discussed...
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 ...
Reference analysis of non-regular models and nonparametric Bayes modeling of large data
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] Bayesian analysis is a principled approach, which makes inference about the parameter, by combining the information gained from the data and the prior ...
Regression analysis of interval-censored failure time data with non proportional hazards models
(University of Missouri--Columbia, 2018)
sieve maximum likelihood procedure. In particular, an EM algorithm is developed and the resulting estimators of regression parameters are shown to be consistent and asymptotically normal. An extensive simulation study was conducted for the assessment...
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 ...
Scalable Bayesian nonparametric learning for biomedical big data
(University of Missouri--Columbia, 2018)
of inference approaches that extend parametric Bayesian models using in finite dimensional distributions. In this dissertation, novel statistical methods based on Bayesian nonparametric models and their extensions are developed to handle 'omics data for various...
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 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 ...
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 ...
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 ...
Hierarchical nonlinear, multivariate, and spatially-dependent time-frequency functional methods
(University of Missouri--Columbia, 2013)
through carefully chosen basis expansions (empirical orthogonal functions) and feature-extraction stochastic search variable selection (SSVS). Properties of the methodology are examined through an extensive simulation study. Finally, we illustrate...
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. ...
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 ...
Statistical-based dynamic machine learning models for nonlinear spatio-temporal processes
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] One of the most vital aspects of any spatio-temporal model is characterizing the dynamics of the process. In both a spatio-temporal forecasting and ...
Regression analysis of longitudinal covariates with censored and longitudinal outcome
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Longitudinal data contain repeated measurements of variables on the same experimental subject. It is often of interest to analyze the relationship ...
Statistical analysis for survival data with missing information
(University of Missouri--Columbia, 2009)
As a branch of statistics, survival analysis, which is often referred to as "reliability theory" in engineering, has a long history. While in practical problems, some information might be missing. This dissertation discusses ...