Bayesian inference for generalized extreme value distributions via Hamiltonian Monte Carlo. Issue 7 (9th August 2017)

• Record Type: Journal Article
• Authors: Hartmann, Marcelo; Ehlers, Ricardo S.
• Is Part Of: Communications in statistics. Volume 46:Issue 7(2017)
• Page Start: 5285
• Page End: 5302
• Publication Date: 2017-08-09
• Subjects: Bayesian approach -- Extreme value -- Hamiltonian Monte Carlo -- Markov chain Monte Carlo -- Riemann manifold; 62F15 -- 62M10; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2016.1152365
• Languages: English
• ISSNs: 0361-0918

Abstract

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.