Electronic Journal of Statistics

Markov chain Monte Carlo estimation of quantiles

Charles R. Doss, James M. Flegal, Galin L. Jones, and Ronald C. Neath

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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.

Article information

Electron. J. Statist., Volume 8, Number 2 (2014), 2448-2478.

First available in Project Euclid: 3 December 2014

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 60J22: Computational methods in Markov chains [See also 65C40] 62M05: Markov processes: estimation

Markov chain Monte Carlo quantile estimation central limit theorem regeneration batch means


Doss, Charles R.; Flegal, James M.; Jones, Galin L.; Neath, Ronald C. Markov chain Monte Carlo estimation of quantiles. Electron. J. Statist. 8 (2014), no. 2, 2448--2478. doi:10.1214/14-EJS957. https://projecteuclid.org/euclid.ejs/1417615759

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