Markov chain Monte Carlo estimation of quantiles

DOI10.1214/14-EJS957zbMath1329.62363arXiv1207.6432MaRDI QIDQ485905

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

Publication date: 14 January 2015

Published in: Electronic Journal of Statistics (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1207.6432

zbMATH Keywords

Monte CarloMarkov chaincentral limit theoremquantile estimationregenerationbatch means

Mathematics Subject Classification ID

Related Items (14)

The Number of MCMC Draws Needed to Compute Bayesian Credible Bounds ⋮ Sampling errors in nested sampling parameter estimation ⋮ Convergence rates of two-component MCMC samplers ⋮ Assessing and Visualizing Simultaneous Simulation Error ⋮ Geometric ergodicity of a more efficient conditional Metropolis-Hastings algorithm ⋮ An MCMC approach to empirical Bayes inference and Bayesian sensitivity analysis via empirical processes ⋮ Some models are useful, but how do we know which ones? Towards a unified Bayesian model taxonomy ⋮ Geometric ergodicity of random scan Gibbs samplers for hierarchical one-way random effects models ⋮ Multivariate initial sequence estimators in Markov chain Monte Carlo ⋮ Quantile Co-Movement in Financial Markets: A Panel Quantile Model With Unobserved Heterogeneity ⋮ Quantifying uncertainty in transdimensional Markov chain Monte Carlo using discrete Markov models ⋮ Convergence analysis of a collapsed Gibbs sampler for Bayesian vector autoregressions ⋮ Berry–Esséen theorem for sample quantiles of asymptotically uncorrelated non reversible Markov chains ⋮ Rank-normalization, folding, and localization: an improved \(\widehat{R}\) for assessing convergence of MCMC (with Discussion)

Uses Software

• mcmcse
• Gibbsit

Cites Work

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