Sköld, M. and Roberts, G. O. (2003) Density Estimation for the Metropolis–Hastings Algorithm. Scandinavian Journal of Statistics, 30 (4). pp. 699-718. ISSN 1467-9469Full text not available from this repository.
Kernel density estimation is an important tool in visualizing posterior densities from Markov chain Monte Carlo output. It is well known that when smooth transition densities exist, the asymptotic properties of the estimator agree with those for independent data. In this paper, we show that because of the rejection step of the Metropolis–Hastings algorithm, this is no longer true and the asymptotic variance will depend on the probability of accepting a proposed move. We find an expression for this variance and apply the result to algorithms for automatic bandwidth selection.
|Journal or Publication Title:||Scandinavian Journal of Statistics|
|Subjects:||Q Science > QA Mathematics|
|Departments:||Faculty of Science and Technology > Lancaster Environment Centre|
|Deposited By:||Mrs Yaling Zhang|
|Deposited On:||26 Jun 2008 09:08|
|Last Modified:||13 Jan 2016 10:26|
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