Please use this identifier to cite or link to this item: http://repositorio.unicamp.br/jspui/handle/REPOSIP/242647
Type: Artigo de periódico
Title: Bayesian Inference On The Memory Parameter For Gamma-modulated Regression Models
Author: Andrade
Plinio; Rifo
Laura; Torres
Soledad; Torres-Aviles
Francisco
Abstract: In this work, we propose a Bayesian methodology to make inferences for the memory parameter and other characteristics under non-standard assumptions for a class of stochastic processes. This class generalizes the Gamma-modulated process, with trajectories that exhibit long memory behavior, as well as decreasing variability as time increases. Different values of the memory parameter influence the speed of this decrease, making this heteroscedastic model very flexible. Its properties are used to implement an approximate Bayesian computation and MCMC scheme to obtain posterior estimates. We test and validate our method through simulations and real data from the big earthquake that occurred in 2010 in Chile.
Subject: Physics, Multidisciplinary
Country: BASEL
Editor: MDPI AG
Citation: Bayesian Inference On The Memory Parameter For Gamma-modulated Regression Models. Mdpi Ag, v. 17, p. 6576-6597 OCT-2015.
Rights: aberto
Identifier DOI: 10.3390/e17106576
Address: http://www.mdpi.com/1099-4300/17/10/6576
Date Issue: 2015
Appears in Collections:Unicamp - Artigos e Outros Documentos

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