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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian Inference on the Memory Parameter for Gamma-Modulated Regression Models

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Author(s):
Andrade, Plinio [1] ; Rifo, Laura [2] ; Torres, Soledad [3] ; Torres-Aviles, Francisco [4]
Total Authors: 4
Affiliation:
[1] Univ Sao Paulo, Inst Math & Stat, BR-05508090 Sao Paulo - Brazil
[2] Univ Estadual Campinas, Inst Math & Stat, BR-13083859 Campinas, SP - Brazil
[3] Univ Valparaiso, CIMFAV, Fac Ingn, Valparaiso 2362905 - Chile
[4] Univ Santiago Chile, Dept Matemat & Ciencia Comp, Santiago 9170022 - Chile
Total Affiliations: 4
Document type: Journal article
Source: Entropy; v. 17, n. 10, p. 6576-6597, OCT 2015.
Web of Science Citations: 0
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. (AU)

FAPESP's process: 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat
Grantee:Jefferson Antonio Galves
Support type: Research Grants - Research, Innovation and Dissemination Centers - RIDC