Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/329945
Type: Artigo
Title: Long-range Dependence And Approximate Bayesian Computation
Author: Andrade
P.; Rifo
L.
Abstract: In this work, we propose a method for estimating the Hurst index, or memory parameter, of a stationary process with long memory in a Bayesian fashion. Such approach provides an approximation for the posterior distribution for the memory parameter and it is based on a simple application of the so-called approximate Bayesian computation (ABC), also known as likelihood-free method. Some popular existing estimators are reviewed and compared to this method for the fractional Brownian motion, for a long-range binary process and for the Rosenblatt process. The performance of our proposal is remarkably efficient.
Subject: Bayesian Inference
Entropy
Hurst Index
Likelihood-free Method
Long-range Dependence
Editor: Taylor & Francis Inc
Philadelphia
Rights: fechado
Identifier DOI: 10.1080/03610918.2014.995816
Address: http://www.tandfonline.com/doi/abs/10.1080/03610918.2014.995816
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

Files in This Item:
File SizeFormat 
000395194600027.pdf1.23 MBAdobe PDFView/Open


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.