| Texto completo | |
| Autor(es): |
Hartmann, Marcelo
;
Ehlers, Ricardo S.
Número total de Autores: 2
|
| Tipo de documento: | Artigo Científico |
| Fonte: | COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION; v. 46, n. 7, p. 5285-5302, 2017. |
| Citações Web of Science: | 1 |
| Resumo | |
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) | |
| Processo FAPESP: | 15/00627-9 - Inferencia Bayesiana, Metropolis-Langevin e Hamiltoniano em Variedades Riemannianas. |
| Beneficiário: | Ricardo Sandes Ehlers |
| Modalidade de apoio: | Bolsas no Exterior - Pesquisa |