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

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Abstract

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: Journal Article
Journal or Publication Title: Scandinavian Journal of Statistics
Uncontrolled Keywords: /dk/atira/pure/researchoutput/libraryofcongress/qa
Subjects:
Departments: Faculty of Science and Technology > Lancaster Environment Centre
ID Code: 81969
Deposited By: ep_importer_pure
Deposited On: 08 Oct 2016 00:06
Refereed?: Yes
Published?: Published
Last Modified: 30 Sep 2019 13:52
URI: https://eprints.lancs.ac.uk/id/eprint/81969

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