Lancaster EPrints

Density Estimation for the Metropolis–Hastings Algorithm.

Sköld, M. and Roberts, G. O. (2003) Density Estimation for the Metropolis–Hastings Algorithm. Scandinavian Journal of Statistics, 30 (4). pp. 699-718. ISSN 1467-9469

Full 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.

Item Type: Article
Journal or Publication Title: Scandinavian Journal of Statistics
Subjects: Q Science > QA Mathematics
Departments: Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 10028
Deposited By: Mrs Yaling Zhang
Deposited On: 26 Jun 2008 09:08
Refereed?: Yes
Published?: Published
Last Modified: 07 Jan 2015 12:34
Identification Number:

Actions (login required)

View Item