| Full text | |
| Author(s): |
Andrade, P.
;
Rifo, L.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 46, n. 2, p. 1219-1237, 2017. |
| Web of Science Citations: | 2 |
| 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. (AU) | |
| FAPESP's process: | 13/07699-0 - Research, Innovation and Dissemination Center for Neuromathematics - NeuroMat |
| Grantee: | Oswaldo Baffa Filho |
| Support Opportunities: | Research Grants - Research, Innovation and Dissemination Centers - RIDC |