Cowles, K. and Roberts, G. O. and Rosenthal, S. (1999) Possible biases induced by MCMC convergence. Journal of Statistical Computation and Simulation, 64 (1). pp. 87-104. ISSN 1563-5163Full text not available from this repository.
Convergence diagnostics are widely used to determine how many initial “burn-in” iterations should be discarded from the output of a Markov chain Monte Carlo (MCMC) sampler in the hope that the remaining samples are representative of the target distribution of interest. This paper demonstrates that some ways of applying convergence diagnostics may actually introduce bias into estimation based on the sampler output. To avoid this possibility, we recommend choosing the number of burn-in iterations r by applying convergence diagnostics to one or more pilot chains, and then basing estimation and inference on a separate long chain from which the first r iterations have been discarded.
|Journal or Publication Title:||Journal of Statistical Computation and Simulation|
|Uncontrolled Keywords:||Markov chain Monte Carlo ; convergence diagnostic ; estimation ; bias ; batch means|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
|Deposited On:||19 Nov 2008 14:14|
|Last Modified:||22 Feb 2017 01:14|
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