Markov chain monte carlo estimation of quantiles

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

Research output: Contribution to journalArticlepeer-review

21 Scopus citations


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 ex- plore the finite sample properties of these methods through examples and provide some recommendations to practitioners.

Original languageEnglish (US)
Pages (from-to)2448-2478
Number of pages31
JournalElectronic Journal of Statistics
StatePublished - 2015

Bibliographical note

Publisher Copyright:
© 2014, Institute of Mathematical Statistics. All rights reserved.


  • Batch means
  • Central limit theorem
  • Markov chain
  • Monte carlo
  • Quantile estimation
  • Regeneration


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