Pseudo-Marginal MCMC for Parameter Estimation in α-Stable Distributions
The 17th IFAC Symposium on System Identification, SYSID
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Riabiz, M., Lindsten, F., & Godsill, S. (2015). Pseudo-Marginal MCMC for Parameter Estimation in α-Stable Distributions. The 17th IFAC Symposium on System Identification, SYSID, 48 (28), 472-477. https://doi.org/10.1016/j.ifacol.2015.12.173
This is the author accepted manuscript. The final version is available from Elsevier via http://dx.doi.org/10.1016/j.ifacol.2015.12.173
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.
External DOI: https://doi.org/10.1016/j.ifacol.2015.12.173
This record's URL: https://www.repository.cam.ac.uk/handle/1810/252625