Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling

Geweke, J; Tanizaki, H

Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling

Geweke, J Tanizaki, H

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Publication Type:: Journal Article
Citation:: Computational Statistics and Data Analysis, 2001, 37 (2), pp. 151 - 170
Issue Date:: 2001-08-28

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Field	Value	Language
dc.contributor.author	Geweke, J	en_US
dc.contributor.author	Tanizaki, H	en_US
dc.date.issued	2001-08-28	en_US
dc.identifier.citation	Computational Statistics and Data Analysis, 2001, 37 (2), pp. 151 - 170	en_US
dc.identifier.issn	0167-9473	en_US
dc.identifier.uri	http://hdl.handle.net/10453/8250
dc.description.abstract	In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state-space modeling in a Bayesian framework, which corresponds to an extension of Carlin et al. (J. Amer. Statist. Assoc. 87(418) (1992) 493-500) and Carter and Kohn (Biometrika 81(3) (1994) 541-553; Biometrika 83(3) (1996) 589-601). Using the Gibbs sampler and the Metropolis-Hastings algorithm, an asymptotically exact estimate of the smoothing mean is obtained from any nonlinear and/or non-Gaussian model. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed Bayes estimator. © 2001 Elsevier Science B.V. All rights reserved.	en_US
dc.relation.ispartof	Computational Statistics and Data Analysis	en_US
dc.relation.isbasedon	10.1016/S0167-9473(01)00009-3	en_US
dc.subject.classification	Statistics & Probability	en_US
dc.title	Bayesian estimation of state-space models using the Metropolis-Hastings algorithm within Gibbs sampling	en_US
dc.type	Journal Article
utslib.citation.volume	2	en_US
utslib.citation.volume	37	en_US
utslib.for	010405 Statistical Theory	en_US
utslib.for	140302 Econometric and Statistical Methods	en_US
utslib.for	1403 Econometrics	en_US
utslib.for	0104 Statistics	en_US
utslib.for	0802 Computation Theory and Mathematics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Business
pubs.organisational-group	/University of Technology Sydney/Faculty of Business/Economics Discipline
utslib.copyright.status	closed_access
pubs.issue	2	en_US
pubs.publication-status	Published	en_US
pubs.volume	37	en_US

Abstract:

In this paper, an attempt is made to show a general solution to nonlinear and/or non-Gaussian state-space modeling in a Bayesian framework, which corresponds to an extension of Carlin et al. (J. Amer. Statist. Assoc. 87(418) (1992) 493-500) and Carter and Kohn (Biometrika 81(3) (1994) 541-553; Biometrika 83(3) (1996) 589-601). Using the Gibbs sampler and the Metropolis-Hastings algorithm, an asymptotically exact estimate of the smoothing mean is obtained from any nonlinear and/or non-Gaussian model. Moreover, taking several candidates of the proposal density function, we examine precision of the proposed Bayes estimator. © 2001 Elsevier Science B.V. All rights reserved.

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http://hdl.handle.net/10453/8250