Title page for ETD etd-06252008-155353

Type of Document Dissertation
Author Li, Zhonggai
URN etd-06252008-155353
Title Objective Bayesian Analysis of Kullback-Liebler Divergence of two Multivariate Normal Distributions with Common Covariance Matrix and Star-shape Gaussian Graphical Model
Degree PhD
Department Statistics
Advisory Committee
Advisor Name Title
Smith, Eric P. Committee Co-Chair
Sun, Dongchu Committee Co-Chair
Du, Pang Committee Member
Morgan, John P. Committee Member
  • Multivariate Normal Distributions
  • Monte Carlo
  • Star-shape Gaussian Graphical Model
  • Objective Priors
  • Jeffreys' Priors
  • Reference Priors
  • Invariant Haar Prior
  • Fisher Information Matrix
  • Frequentist Matching
  • Kullback-Liebler Divergence
Date of Defense 2008-06-18
Availability unrestricted
This dissertation consists of four independent but related parts, each in a Chapter. The first part is an introductory. It serves as the background introduction and offer preparations for later parts. The second part discusses two population multivariate normal distributions with common covariance matrix. The goal for this part is to derive objective/non-informative priors for the parameterizations and use these priors to build up constructive random posteriors of the Kullback-Liebler (KL) divergence of the two multivariate normal populations, which is proportional to the distance between the two means, weighted by the common precision matrix. We use the Cholesky decomposition for re-parameterization of the precision matrix. The KL divergence is a true distance measurement for divergence between the two multivariate normal populations with common covariance matrix. Frequentist properties of the Bayesian procedure using these objective priors are studied through analytical and numerical tools. The third part considers the star-shape Gaussian graphical model, which is a special case of undirected Gaussian graphical models. It is a multivariate normal distribution where the variables are grouped into one "global" group of variable set and several "local" groups of variable set. When conditioned on the global variable set, the local variable sets are independent of each other. We adopt the Cholesky decomposition for re-parametrization of precision matrix and derive Jeffreys' prior, reference prior, and invariant priors for new parameterizations. The frequentist properties of the Bayesian procedure using these objective priors are also studied. The last part concentrates on the discussion of objective Bayesian analysis for partial correlation coefficient and its application to multivariate Gaussian models.
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