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| Titel |
Bayesian analysis of heavy-tailed and long-range dependent Processes |
| VerfasserIn |
Timothy Graves, Nick Watkins, Robert Gramacy, Christian Franzke |
| Konferenz |
EGU General Assembly 2014
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| Medientyp |
Artikel
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| Sprache |
Englisch
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| Digitales Dokument |
PDF |
| Erschienen |
In: GRA - Volume 16 (2014) |
| Datensatznummer |
250093207
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| Publikation (Nr.) |
 EGU/EGU2014-7730.pdf |
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| Zusammenfassung |
| We have used MCMC algorithms to perform a Bayesian analysis of Auto-Regressive
Fractionally-Integrated Moving-Average ARFIMA(p,d,q) processes, which are capable of
modelling long range dependence (e.g. Beran et al, 2013). Our principal aim is to obtain
inference about the long memory parameter, d, with secondary interest in the scale and
location parameters. We have developed a reversible-jump method enabling us to integrate
over different model forms for the short memory component. We initially assume
Gaussianity, and have tested the method on both synthetic and physical time series. We have
extended the ARFIMA model by weakening the Gaussianity assumption, assuming an
alpha-stable, heavy tailed, distribution for the innovations, and performing joint inference on
d and alpha. We will present a study of the dependence of the posterior variance
of the memory parameter d on the length of the time series considered. This will
be compared with equivalent error diagnostics for other popular measures of d. |
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