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Pseudo-Marginal MCMC for Parameter Estimation in Alpha-Stable Distributions
Signal Processing and Communications Laboratory, Engineering Department, University of Cambridge, UK.
Signal Processing and Communications Laboratory, Engineering Department, University of Cambridge, UK.
Signal Processing and Communications Laboratory, Engineering Department, University of Cambridge, UK.
2015 (English)In: Proceedings of the 17th IFAC Symposium on System Identification (SYSID), Elsevier, 2015, Vol. 48, p. 472-477Conference paper, Published paper (Refereed)
Abstract [en]

The α-stable distribution is very useful for modelling data with extreme values and skewed behaviour. The distribution is governed by two key parameters, tail thickness and skewness, in addition to scale and location. Inferring these parameters is difficult due to the lack of a closed form expression of the probability density. We develop a Bayesian method, based on the pseudo-marginal MCMC approach, that requires only unbiased estimates of the intractable likelihood. To compute these estimates we build an adaptive importance sampler for a latentvariable- representation of the α-stable density. This representation has previously been used in the literature for conditional MCMC sampling of the parameters, and we compare our method with this approach.

Place, publisher, year, edition, pages
Elsevier, 2015. Vol. 48, p. 472-477
Series
FAC Proceedings Volumes (IFAC-PapersOnline), ISSN 2405-8963 ; 48:28
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:liu:diva-159492DOI: 10.1016/j.ifacol.2015.12.173Scopus ID: 2-s2.0-84988532373OAI: oai:DiVA.org:liu-159492DiVA, id: diva2:1344732
Conference
17th IFAC Symposium on System Identification (SYSID), Beijing, China, 19–21 October 2015
Available from: 2019-08-21 Created: 2019-08-21 Last updated: 2019-08-27Bibliographically approved

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CiteExportLink to record
Permanent link

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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
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Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
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More languages
Output format
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