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|Title:||Long-range Dependence And Approximate Bayesian Computation|
|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.|
|Editor:||Taylor & Francis Inc|
|Citation:||Communications In Statistics-theory And Methods. Taylor & Francis Inc, v. 46, p. 1219 - 1237, 2017.|
|Appears in Collections:||Unicamp - Artigos e Outros Documentos|
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