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Bayesian inference for the bivariate extreme model


Mohd Amin, Nor Azrita and Adam, Mohd Bakri (2016) Bayesian inference for the bivariate extreme model. In: 2nd International Conference on Mathematics, Engineering and Industrial Applications 2016 (ICoMEIA 2016), 10-12 Aug. 2016, Songkhla, Thailand. (pp. 1-8).


The bivariate extreme distribution based on logistic dependence function is used to model the extreme observations of two different variables. The model is used in a Bayesian framework where no information of prior is available on unknown model parameters. Maximum likelihood method and a Markov chain Monte Carlo (MCMC) technique, Multiple-try Metropolis algorithm are implemented into the data analysis. MTM algorithm is the new alternative in the field of Bayesian extremes for summarizing the posterior distribution. Using simulation study, the capability of MTM algorithm to analyze the posterior distribution is implement. The proposed theoretical methods apply to extreme particulate matter data from two air monitoring stations in Johor.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Institute for Mathematical Research
DOI Number: https://doi.org/10.1063/1.4965217
Publisher: AIP Publishing
Keywords: Bayesian framework; Bivariate extreme model; MTM algorithm
Depositing User: Nabilah Mustapa
Date Deposited: 24 Oct 2017 05:35
Last Modified: 24 Oct 2017 05:35
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4965217
URI: http://psasir.upm.edu.my/id/eprint/57574
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