期刊
JOURNAL OF STATISTICAL COMPUTATION AND SIMULATION
卷 84, 期 4, 页码 833-849出版社
TAYLOR & FRANCIS LTD
DOI: 10.1080/00949655.2012.729588
关键词
Hellinger distance; kernel density estimation; Markov chain Monte Carlo; Bayesian robustness
Bayesian analysis often requires the researcher to employ Markov Chain Monte Carlo (MCMC) techniques to draw samples from a posterior distribution which in turn is used to make inferences. Currently, several approaches to determine convergence of the chain as well as sensitivities of the resulting inferences have been developed. This work develops a Hellinger distance approach to MCMC diagnostics. An approximation to the Hellinger distance between two distributions f and g based on sampling is introduced. This approximation is studied via simulation to determine the accuracy. A criterion for using this Hellinger distance for determining chain convergence is proposed as well as a criterion for sensitivity studies. These criteria are illustrated using a dataset concerning the Anguilla australis, an eel native to New Zealand.
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