Pseudo-Marginal MCMC for Parameter Estimation in Alpha-Stable Distributions [Elektronisk resurs]
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Riabiz, Marina (författare)
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17th IFAC Symposium on System Identification (SYSID)
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Lindsten, Fredrik, 1984- (författare)
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Godsill, Simon J. (författare)
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Linköpings universitet Tekniska fakulteten (utgivare)
- Publicerad: Elsevier, 2015
- Engelska.
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Serie: FAC Proceedings Volumes (IFAC-PapersOnline), 2405-8963 2405-8963
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Ingår i: Proceedings of the 17th IFAC Symposium on System Identification (SYSID). ; 472-477
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- Relaterad länk:
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http://www.liu.se (Värdpublikation)
Sammanfattning
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- 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
Ämnesord
- Natural Sciences (hsv)
- Mathematics (hsv)
- Probability Theory and Statistics (hsv)
- Naturvetenskap (hsv)
- Matematik (hsv)
- Sannolikhetsteori och statistik (hsv)
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- government publication (marcgt)
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