Markov Chain Monte Carlo Estimation of Quantiles

Abstract

We consider quantile estimation using Markov chain Monte Carlo and establish conditions under which the sampling distribution of the Monte Carlo error is approximately Normal. Further, we investigate techniques to estimate the associated asymptotic variance, which enables construction of an asymptotically valid interval estimator. Finally, we explore the finite sample properties of these methods through examples and provide some recommendations to practitioners.

35 pages, 1 figure

References in corpus (3)

• Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?
• Renewal theory and computable convergence rates for geometrically ergodic Markov chains
• Relative fixed-width stopping rules for Markov chain Monte Carlo simulations

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