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Posterior mean and variance approximation for regression and time series problems
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Triantafyllopoulos, K. and Harrison, P. J. (2008) Posterior mean and variance approximation for regression and time series problems. Statistics , Vol.42 (No.4). pp. 329-350. doi:10.1080/02331880701864978 ISSN 0233-1888.
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Official URL: http://dx.doi.org/10.1080/02331880701864978
Abstract
This paper develops a methodology for approximating the posterior first two moments of the posterior distribution in Bayesian inference. Partially specified probability models that are defined only by specifying means and variances, are constructed based upon second-order conditional independence in order to facilitate posterior updating and prediction of required distributional quantities. Such models are formulated particularly for multivariate regression and time series analysis with unknown observational variance-covariance components. The similarities and differences of these models with the Bayes linear approach are established. Several subclasses of important models, including regression and time series models with errors following multivariate t, inverted multivariate t and Wishart distributions, are discussed in detail. Two numerical examples consisting of simulated data and of US investment and change in inventory data illustrate the proposed methodology.
Item Type: | Journal Article | ||||
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Subjects: | Q Science > QA Mathematics | ||||
Divisions: | Faculty of Science, Engineering and Medicine > Science > Statistics | ||||
Journal or Publication Title: | Statistics | ||||
Publisher: | Taylor & Francis Ltd. | ||||
ISSN: | 0233-1888 | ||||
Official Date: | 2008 | ||||
Dates: |
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Volume: | Vol.42 | ||||
Number: | No.4 | ||||
Number of Pages: | 22 | ||||
Page Range: | pp. 329-350 | ||||
DOI: | 10.1080/02331880701864978 | ||||
Status: | Peer Reviewed | ||||
Publication Status: | Published | ||||
Access rights to Published version: | Restricted or Subscription Access |
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