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Journal of Statistical Computation and Simulation Volume 64, 1999 - Issue 1
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Original Articles

Possible biases induced by mcmc convergence diagnostics

Mary Kathryn. Cowles Department of Statistics and Actuarial Science, University of Iowa, Iowa City, IA, 52242-1419, USA
,
Gareth O. Roberts Department of Mathematics and Statistics, Fylde College, Lancaster University, LA1 4YF, Lancaster, England
&
Jeffrey S. Rosenthal Department of Statistics, University of Toronto, M5S 3G3, Partially supported by NSERC of Canada, Toronto, Ontario, Canada
Pages 87-104 | Received 30 Oct 1998, Published online: 20 Mar 2007

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Y Fan, S. P Brooks & A Gelman. (2006) Output Assessment for Monte Carlo Simulations via the Score Statistic. Journal of Computational and Graphical Statistics 15:1, pages 178-206.
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Hiroshi Hojo. (2011) AN MCMC APPROACH TO ESTIMATING THE WANDERING IDEAL POINT MODEL FOR “pick any/n” TYPE OF BINARY DATA. Behaviormetrika 38:1, pages 97-124.
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James M. Flegal, Murali Haran & Galin L. Jones. (2008) Markov Chain Monte Carlo: Can We Trust the Third Significant Figure?. Statistical Science 23:2.
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Jeff Gill. (2007) Is Partial-Dimension Convergence a Problem for Inferences from MCMC Algorithms?. Political Analysis 16:2, pages 153-178.
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Galin L. Jones & James P. Hobert. (2004) Sufficient burn-in for Gibbs samplers for a hierarchical random effects model. The Annals of Statistics 32:2.
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Gareth O. Roberts & Jeffrey S. Rosenthal. (2004) General state space Markov chains and MCMC algorithms. Probability Surveys 1:none.
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