A markov chain monte carlo convergence diagnostic using subsampling
Date
1999ISSN
1061-8600Source
Journal of Computational and Graphical StatisticsVolume
8Issue
3Pages
431-451Google Scholar check
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A new diagnostic procedure for assessing convergence of a Markov chain Monte Carlo (MCMC) simulation is proposed. The method is based on the use of subsampling for the construction of confidence regions from asymptotically stationary time series as developed in Politis, Romano, and Wolf. The MCMC subsampling diagnostic is capable of gauging at what point the chain has “forgotten” its starting points, as well as to indicate how many points are needed to estimate the parameters of interest according to the desired accuracy. Simulation examples are also presented showing that the diagnostic performs favorably in interesting cases. © 1999 American Statistical Association, Institute of Mathematical Statistics, and Interface Foundation of North America.