A Markov Chain Monte Carlo Convergence Diagnostic Using Subsampling |
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| Authors: | S G Giakoumatos I D Vrontos P Dellaportas D N Politis |
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| Institution: | 1. Department of Statistics , Athens University of Economics and Business , Patission 76, 10434, Athens , Greece;2. Department of Mathematics , University of California at San Diego , La Jolla , CA , 92093-0112 , USA |
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| Abstract: | Abstract 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. |
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| Keywords: | Asymptotic stationarity Confidence regions Strong mixing |
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