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Bayesian and machine learning models for dependent data with applications to official statistics and survey methodology
(University of Missouri--Columbia, 2023)
[EMBARGOED UNTIL 8/1/2024] Small Area estimation has garnered much interest in recent times by both private entities as well government agencies as means of public policy guidance, formulating programs for regional and ...
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 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 ...
Nonstationary Bayesian time series models with time-varying parameters and regime-switching
(University of Missouri--Columbia, 2021)
Nonstationary time series data exist in various scientic disciplines, including environmental science, biology, signal processing, econometrics, among others. Many Bayesian models have been developed to handle nonstationary ...