Abstract Details
Activity Number:
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463
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Type:
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Invited
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Date/Time:
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Wednesday, August 12, 2015 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #314522
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View Presentation
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Title:
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High-Dimensional MCMC Output Analysis
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Author(s):
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Galin Jones* and Dootika Vats and James M. Flegal
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Companies:
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University of Minnesota and University of Minnesota and UC Riverside
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Keywords:
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Monte Carlo ;
Markov chain ;
standard error ;
batch means ;
Bayesian computation
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Abstract:
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We consider calculating a Monte Carlo standard error (MCSE) when the the number of quantities being estimated via MCMC is large. In particular, we base the MCSE on an estimate of the covariance matrix of an asymptotic multivariate normal distribution We consider spectral and batch means methods for estimating this matrix and establish strong consistency of the resulting estimators. Conditions for convergence of the eigenvalues of the batch means and spectral estimators is also addressed. Using these theoretical results we develop rigorous stopping rules for terminating the simulation effort based on both fixed-width and relative fixed-width methods. Finally, we consider the finite sample properties of the methods in several examples and discuss possible extensions. This is joint work with James Flegal and Dootika Vats.
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Authors who are presenting talks have a * after their name.
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