Electronic Journal of Statistics

Markov chain Monte Carlo estimation of quantiles

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

Full-text: Open access

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.

Article information

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

Dates
First available in Project Euclid: 3 December 2014

Permanent link to this document
http://projecteuclid.org/euclid.ejs/1417615759

Digital Object Identifier
doi:10.1214/14-EJS957

Mathematical Reviews number (MathSciNet)
MR3285872

Zentralblatt MATH identifier
1329.62363

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

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

Citation

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. http://projecteuclid.org/euclid.ejs/1417615759.


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