文章基本信息

标题：Pseudo-Marginal MCMC for Parameter Estimation in α-Stable Distributions *
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作者：Marina Riabiz ; Fredrik Lindsten ; Simon Godsill 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2015
卷号：48
期号：28
页码：472-477
DOI：10.1016/j.ifacol.2015.12.173
语种：English
出版社：Elsevier
摘要：AbstractThe 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.