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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...
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...
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...
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 ...
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. ...
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 ...