| Full text | |
| Author(s): |
Hartmann, Marcelo
;
Ehlers, Ricardo S.
Total Authors: 2
|
| Document type: | Journal article |
| Source: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 46, n. 7, p. 5285-5302, 2017. |
| Web of Science Citations: | 1 |
| Abstract | |
In this article, we propose to evaluate and compare Markov chain Monte Carlo (MCMC) methods to estimate the parameters in a generalized extreme value model. We employed the Bayesian approach using traditional Metropolis-Hastings methods, Hamiltonian Monte Carlo (HMC), and Riemann manifold HMC (RMHMC) methods to obtain the approximations to the posterior marginal distributions of interest. Applications to real datasets and simulation studies provide evidence that the extra analytical work involved in Hamiltonian Monte Carlo algorithms is compensated by a more efficient exploration of the parameter space. (AU) | |
| FAPESP's process: | 15/00627-9 - Bayesian Inference, Metropolis-Langevin and Hamiltonian in Riemann manifolds. |
| Grantee: | Ricardo Sandes Ehlers |
| Support Opportunities: | Scholarships abroad - Research |