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Bayesian Theory and Applications$
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Paul Damien, Petros Dellaportas, Nicholas G. Polson, and David A. Stephens

Print publication date: 2013

Print ISBN-13: 9780199695607

Published to Oxford Scholarship Online: May 2013

DOI: 10.1093/acprof:oso/9780199695607.001.0001

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PRINTED FROM OXFORD SCHOLARSHIP ONLINE (oxford.universitypressscholarship.com). (c) Copyright Oxford University Press, 2021. All Rights Reserved. An individual user may print out a PDF of a single chapter of a monograph in OSO for personal use. date: 22 April 2021

Dynamic and spatial modelling of block maxima extremes

Dynamic and spatial modelling of block maxima extremes

(p.183) 10 Dynamic and spatial modelling of block maxima extremes
Bayesian Theory and Applications

Gabriel Huerta

Glenn A. Stark

Oxford University Press

This chapter develops a general class of models to study extreme values based on the Generalized Extreme Value (GEV) distribution. They rely on time domain and spatial latent components. These models were not available until recently and their implementations are made possible by the development of Markov chain Monte Carlo (MCMC) approaches. Using as a starting point rainfall data at the Maíquetia–Simon Bolivar airport near Caracas, Venezuela, for the period 1960–1999, the chapter addresses two key questions: How do we characterize these rainfall events? and How do we assess for the non-stationary behaviour that is apparent in the data?

Keywords:   precipitation, Generalized Extreme Value distribution, rainfall events, non-stationary behaviour, Markov chain Monte Carlo methods

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