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Type: Artigo
Title: Long-range Dependence And Approximate Bayesian Computation
Author: Andrade
P.; Rifo
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
Hurst Index
Likelihood-free Method
Long-range Dependence
Editor: Taylor & Francis Inc
Citation: Communications In Statistics-theory And Methods. Taylor & Francis Inc, v. 46, p. 1219 - 1237, 2017.
Rights: fechado
Identifier DOI: 10.1080/03610918.2014.995816
Date Issue: 2017
Appears in Collections:Unicamp - Artigos e Outros Documentos

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