Dissertation/Thesis Abstract

The development of a convergence diagnostic for Markov chain Monte Carlo estimation
by Burke, Matthew Joseph, Ph.D., The University of North Carolina at Greensboro, 2011, 282; 3490550
Abstract (Summary)

The development and investigation of a convergence diagnostic for Markov Chain Monte Carlo (MCMC) posterior distributions is presented in this paper. The current method is an adaptation of an existing convergence diagnostic based on the Cumulative Sum (CUSUM, Page 1954; Yu & Mykland, 1998; Brooks, 1998c) procedure. The diagnostic under development is seen to be an improvement over the technique upon which it is based because it offers a simple way to remove one of the two major assumptions made by the previous method, namely that the shape of the distribution under consideration is symmetric. Results are mixed, but there is some evidence to indicate that the new technique is sensitive to the degree of autocorrelation present and the stability of the chains. Also, the new diagnostic behaves differently than three existing convergence diagnostics.

Indexing (document details)
Advisor: Henson, Robert A., Luecht, Richard M.
Commitee: Ackerman, Terry A., Richter, Scott J., Willse, John T.
School: The University of North Carolina at Greensboro
Department: School of Education: Educational Research Methodology
School Location: United States -- North Carolina
Source: DAI-A 73/04, Dissertation Abstracts International
Subjects: Educational tests & measurements, Statistics
Keywords: Convergence diagnostic, Markov chain Monte Carlo
Publication Number: 3490550
ISBN: 978-1-267-11540-9
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