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Empirical likelihood approach estimation of structural equation models
(University of Missouri--Columbia, 2007)
This thesis provides a preliminary investigation of empirical likelihood approach estimation of structural equation models. An auxiliary variable approach built on general estimating equation methods in the EL settings is ...
Bayesian cusp regression and linear mixed model
(University of Missouri--Columbia, 2022)
First of all, we introduce the Bayesian mixture way of solving the Cusp Catastrophe model, which is designed to deal with piece-wise continuous outcomes. Simulation and real data analysis show that the new method beats the ...
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 ...
Hierarchical physical-statistical forecasting in the atmospheric sciences
(University of Missouri--Columbia, 2009)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A class of hierarchical Bayesian models is introduced for Physical-Statistical forecasting purposes in the Atmospheric Sciences. The first project describes a methodological...
Point processes on the complex plane with applications
(University of Missouri--Columbia, 2019)
[ACCESS RESTRICTED TO THE UNIVERSITY OF MISSOURI AT REQUEST OF AUTHOR.] A point process is a random collection of points from a certain space, and point process models are widely used in areas dealing with spatial data. However, studies of point...
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 ...
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 ...