| Texto completo | |
| Autor(es): |
Andrade, P.
;
Rifo, L.
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 46, n. 2, p. 1219-1237, 2017. |
| Citações Web of Science: | 2 |
| Resumo | |
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. (AU) | |
| Processo FAPESP: | 13/07699-0 - Centro de Pesquisa, Inovação e Difusão em Neuromatemática - NeuroMat |
| Beneficiário: | Oswaldo Baffa Filho |
| Modalidade de apoio: | Auxílio à Pesquisa - Centros de Pesquisa, Inovação e Difusão - CEPIDs |