Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo

Show full item record



Permalink

http://hdl.handle.net/10138/312419

Citation

Hartmann , M & Ehlers , R S 2017 , ' Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo ' , Communications in Statistics: Simulation and Computation , vol. 46 , no. 7 , pp. 5285-5302 . https://doi.org/10.1080/03610918.2016.1152365

Title: Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo
Author: Hartmann, Marcelo; Ehlers, Ricardo S.
Contributor organization: Research Centre for Ecological Change
Department of Mathematics and Statistics
Environmental and Ecological Statistics Group
Date: 2017
Language: eng
Number of pages: 18
Belongs to series: Communications in Statistics: Simulation and Computation
ISSN: 0361-0918
DOI: https://doi.org/10.1080/03610918.2016.1152365
URI: http://hdl.handle.net/10138/312419
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.
Subject: 112 Statistics and probability
Peer reviewed: Yes
Usage restriction: openAccess
Self-archived version: acceptedVersion


Files in this item

Total number of downloads: Loading...

Files Size Format View
1410.4534.pdf 336.6Kb PDF View/Open

This item appears in the following Collection(s)

Show full item record