This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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347
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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IMS
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| Abstract - #308894 |
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Title:
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Quantile Estimation via Markov Chain Monte Carlo
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Author(s):
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James M. Flegal*+
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Companies:
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University of California, Riverside
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Address:
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2670 STAT/COMP, Riverside, CA, 92521, United States
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Keywords:
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MCMC ;
Standard Errors ;
Quantiles
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Abstract:
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This article considers quantile estimation based on Markov chain Monte Carlo (MCMC) simulations. Quantile estimates are commonly reported to summarize features of a given target distribution, most often in the form of Bayesian credible region endpoints. Using estimates from the inverse of the empirical distribution function, we establish a central limit theorem (CLT) enabling assessment the associated uncertainty. Further, we propose the use of the subsampling bootstrap and regeneration to estimate the associated asymptotic variance. Finally, we investigate the finite sample properties of these methods through three examples and provide recommendations to practitioners.
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Authors who are presenting talks have a * after their name.
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