Likelihood-free Bayesian inference for α-stable models

被引:27
作者
Peters, G. W. [1 ]
Sisson, S. A. [1 ]
Fan, Y. [1 ]
机构
[1] Univ New S Wales, Sch Math & Stat, Sydney, NSW 2052, Australia
基金
澳大利亚研究理事会;
关键词
alpha-stable distributions; Approximate Bayesian computation; Bayesian inference; Likelihood-free inference; Multivariate models; SEQUENTIAL MONTE-CARLO; APPROXIMATION; DISTRIBUTIONS; MCMC;
D O I
10.1016/j.csda.2010.10.004
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
alpha-stable distributions are utilized as models for heavy-tailed noise in many areas of statistics, finance and signal processing engineering. However, in general, neither univariate nor multivariate alpha-stable models admit closed form densities which can be evaluated pointwise. This complicates the inferential procedure. As a result, alpha-stable models are practically limited to the univariate setting under the Bayesian paradigm, and to bivariate models under the classical framework. A novel Bayesian approach to modelling univariate and multivariate alpha-stable distributions is introduced, based on recent advances in "likelihood-free" inference. The performance of this procedure is evaluated in 1, 2 and 3 dimensions, and through an analysis of real daily currency exchange rate data. The proposed approach provides a feasible inferential methodology at a moderate computational cost. (C) 2010 Elsevier B.V. All rights reserved.
引用
收藏
页码:3743 / 3756
页数:14
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