Parameter estimation of Alpha-Stable Distributions Based on MCMC

摘要：

The a-stable distribution is a very flexible tool to model NonGaussian data. Stable distributions can allow for modeling infinite variance, skewness and heavy tails, but gives rise to inferential problems related to the estimation of the stable distribution parameters. In this work, we study the estimation of a -stable distributions using numerical Bayesian sampling techniques such as Markov chain Monte Carlo (MCMC), which can simultaneously estimate the four parameters of the model with good performance. Metropolis-Hastings algorithm is used to update the parameters of a -stable distribution at every iteration. The simulation results show that our estimation method is capable of estimating all the parameters accurately.

关键词： Alpha Stable distributions MCMC Metropolis-Hastings algorithm

作者: HAO Yan-ling SHAN Zhi-ming SHEN Feng LV Dong-ze

作者单位: College of Automation Harbin Engineering University Harbin, China

会议类型: 国际会议

会议名称: 2011 3rd International Conference on Advanced Computer Control(2011年IEEE第三届高端计算机控制国际会议 ICACC2011)

会议地点: 哈尔滨

会议语种:英文

页码: 325-327

在线出版日期: 2011-01-18（万方平台首次上网日期，不代表论文的发表时间）