Monte Carlo standard errors for Markov chain Monte Carlo

by Flegal, James Marshall, Ph.D., UNIVERSITY OF MINNESOTA, 2008, 191 pages; 3316137


Markov chain Monte Carlo (MCMC) is a method of producing a correlated sample to estimate characteristics of a target distribution. A fundamental question is how long should the simulation be run? One method to address this issue is to run the simulation until the width of a confidence interval for the quantity of interest is below a user-specified value. The use of this fixed-width methods requires an estimate of the Monte Carlo standard error (MCSE). This dissertation begins by discussing why MCSEs are important, how they can be easily calculated in MCMC and how they can be used to decide when to stop the simulation. The use of MCSEs is then compared to a popular alternative in the context of multiple examples.

This dissertation continues by discussing the relevant Markov chain theory with particular attention paid to the conditions and definitions needed to establish a Markov chain central limit theorem. Estimating MCSEs requires estimating the associated asymptotic variance. I introduce several techniques for estimating MCSEs: batch means, overlapping batch means, regeneration, subsampling and spectral variance estimation. Asymptotic properties useful in MCMC settings are established for these variance estimators. Specifically, I established conditions under which the estimator for the asymptotic variance in a Markov chain central limit theorem is strongly consistent. Strong consistency ensures that confidence intervals formed will be asymptotically valid. In addition, I established conditions to ensure mean-square consistency for the estimators using batch means and overlapping batch means. Mean-square consistency is useful in choosing an optimal batch size for MCMC practitioners.

Several approaches have been introduced dealing with the special case of estimating ergodic averages and their corresponding standard errors. Surprisingly, very little attention has been given to characteristics of the target distribution that cannot be represented as ergodic averages. To this end, I proposed use of subsampling methods as a means for estimating the qth quantile of the posterior distribution. Finally, the finite sample properties of subsampling are examined.

AdvisersGalin L. Jones; Glen D. Meeden
Source TypeDissertation
Publication Number3316137

About ProQuest Dissertations & Theses
With nearly 4 million records, the ProQuest Dissertations & Theses (PQDT) Global database is the most comprehensive collection of dissertations and theses in the world. It is the database of record for graduate research.

PQDT Global combines content from a range of the world's premier universities - from the Ivy League to the Russell Group. Of the nearly 4 million graduate works included in the database, ProQuest offers more than 2.5 million in full text formats. Of those, over 1.7 million are available in PDF format. More than 90,000 dissertations and theses are added to the database each year.

If you have questions, please feel free to visit the ProQuest Web site - - or contact ProQuest Support.