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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 ...
Bayesian hierarchical modeling of colorectal and breast cancer data in Missouri
(University of Missouri--Columbia, 2018)
in Missouri were studied with emphasis on different groups of people categorized by age, gender and county at diagnosis. The incidence and mortality data were aggregated into different spatial regions due to data confidential requirements, which was identified...
Statistical analysis of clustered or multivariate interval-censored failure time data
(University of Missouri--Columbia, 2018)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT AUTHOR'S REQUEST.] Interval-censored failure time data are a type of the failure time data that often occur in clinical trials with periodic follow-ups among others. In ...
Bayesian spatial models for adjusting nonresponse in small area estimation
(University of Missouri--Columbia, 2010)
auxiliary information such as hunter age and number of deer harvested. We confirm that nonresponse is nonignorable in our example. The estimates of satisfaction rates after adjusting for nonresponse are lower than those without consideration of nonresponse....
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 ...
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 ...
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 ...
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 ...