Systematic inference of the long-range dependence and heavy-tail distribution parameters of ARFIMA models

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• 作者：Timothy Graves^a ; Christian L.E. Franzke^b ; ^{christian.franzke@uni-hamburg.de} ; Nicholas W. Watkins^c ; ^d ; ^e ; ^f ; Robert B. Gramacy^g ; Elizabeth Tindale^d
• 关键词：Long-range dependence ; Heavy-tails ; Bayesian estimation ; ARFIMA
• 刊名：Physica A: Statistical Mechanics and its Applications
• 出版年：2017
• 出版时间：1 May 2017
• 年：2017
• 卷：473
• 期：Complete
• 页码：60-71
• 全文大小：567 K
• 卷排序：473

文摘

Self-similarity has two contributions: LRD and heavy-tailed jumps. Systematic simultaneous estimation of LRD and heavy-tail distribution parameters. Development of a novel Bayesian method for estimation of these two parameters. Method flexible to allow choice of heavy-tailed distribution. Successful demonstration of effectiveness and accuracy on synthetic data.