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Variable selection for interval-censored and functional survival data
(University of Missouri--Columbia, 2022)
Interval-censored data are a type of failure time data that is only known to belong to a time interval but cannot be observed precisely. Note that interval-censoring is often encountered in medical or health studies with ...
Data combining using mixtures of g-priors with application on county-level female breast cancer prevalence
(University of Missouri--Columbia, 2022)
As more and more data are available, data synthesis has become an indispensable task for researchers. From a Bayesian perspective, this dissertation includes three related projects and aims at quantifying the benefits of ...
A Bayesian approach to data-driven discovery of nonlinear dynamic equations
(University of Missouri--Columbia, 2022)
Dynamic equations parameterized by differential equations are used to represent a variety of real-world processes. The equations used to describe these processes are generally derived based on physical principles and a ...
Modeling state duration and emission dependence in hidden Markov and hidden semi-Markov models
(University of Missouri--Columbia, 2022)
Hidden Markov models (HMM) are composed of a latent state sequence and an observation sequence conditionally independent on the states, which follows an emission distribution. Hidden semi-Markov models (HSMM) extend the ...
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 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 ...
Modeling spatio-temporal data using a Bayesian probabilistic cellular automata framework
(University of Missouri--Columbia, 2023)
Regularly gridded, or cellular, discrete-valued spatio-temporal data are common in many application areas. Such data can be considered from many perspectives, including deterministic or stochastic cellular automata, where ...
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 ...
A class of weakly informative prior on multinomial logistic regression with separated data
(University of Missouri--Columbia, 2021)
Complete separation in logistic regression, sometimes referred to as perfect prediction, occurs when the outcome variable completely separates predictor variables. The likelihood function is monotonically increasing on the ...
Dynamic spatial-temporal point process models via conditioning
(University of Missouri--Columbia, 2021)
We propose and investigate dynamic spatial-temporal point process models for independent and interacting events. The models for independent events are dynamic spatial-temporal Poisson point process (DSTPPP) model that ...
Bayesian unit-level modeling of non-Gaussian survey data under informative sampling with application to small area estimation
(University of Missouri--Columbia, 2021)
Unit-level models are an alternative to the traditional area-level models used in small area estimation, characterized by the direct modeling of survey responses rather than aggregated direct estimates. These unit-level ...
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 model averaging for mathematics achievement growth rate trends
(University of Missouri--Columbia, 2022)
In this study, we investigated the use of Bayesian model averaging (BMA) for latent growth curve models. We used the Trends in International Mathematics and Science Study (TIMSS) to predict growth rates in 8th-grade students' ...
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 ...
Average treatment effect evaluation with time-to-event data in randomized clinical trials and observational studies
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] The average treatment effect (ATE) is defined as the difference in the expected outcome between individuals receiving the treatment and those not receiving it. As a measure of the impact of a ...
Methodologies for low-rank analysis and regionalization for multi-scale spatial datasets
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 5/1/2024] This dissertation comprises three chapters that focus on developing low-rank modeling and spatial aggregation techniques to overcome the computational and storage challenges associated with ...
Dynamic analysis of complex panel count data
(University of Missouri--Columbia, 2021)
Panel count data occur in many fields including clinical, demographical and industrial studies and an extensive literature has been established for their regression analysis. However, most of the existing methods apply ...
Statistical methods to deflect allele specific expression, alterations of allele specific expression and differential expression
(University of Missouri--Columbia, 2021)
The advent of next-generation sequencing (NGS) technology has facilitated the recent development of RNA sequencing (RNA-seq), which is a novel mapping and quantifying method for transcriptomes. By RNA-seq, one can measure ...
Regression analysis of correlated interval-censored failure time data with a cured subgroup
(University of Missouri--Columbia, 2021)
Interval-censored failure time data commonly occur in many periodic follow-up studies such as epidemiological experiments, medical studies and clinical trials. By intervalcensored data, we usually mean that one cannot ...
Semiparametric analysis of time-to-event data and longitudinal data
(University of Missouri--Columbia, 2021)
Interval-censored failure time data are commonly observed in demographical, epi-demiological, financial, medical, and sociological studies. It is well-known that the proportional hazards model is one of the most used ...