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Bayesian extreme for modeling high PM10 concentration in Johor


Mohd Amin, Nor Azrita and Adam, Mohd Bakri and Aris, Ahmad Zaharin (2015) Bayesian extreme for modeling high PM10 concentration in Johor. Procedia Environmental Sciences, 30. pp. 309-314. ISSN 1878-0296


The aim of this study is to determine the behavior of extreme PM10 levels monitored at three air monitoring stations in Johor using frequentist and Bayesian technique. Bayesian allows priors or additional information about the data into the analysis which expectedly improve the model fit. The generalized extreme value distribution is fitted to the monthly maxima PM10 data. The results obtained show that the Bayesian posterior inferences perform at least as trustworthy as maximum likelihood estimates but considerably more flexible and informative. The return levels for 10, 50 and 100-years were computed for future prediction.

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Additional Metadata

Item Type: Article
Divisions: Faculty of Environmental Studies
Institute for Mathematical Research
DOI Number: https://doi.org/10.1016/j.proenv.2015.10.055
Publisher: Elsevier
Keywords: Extreme value theory; PM10; Generalized extreme value distribution; Bayesian inference; Maximum likelihood estimates
Depositing User: Mas Norain Hashim
Date Deposited: 03 May 2016 02:15
Last Modified: 03 May 2016 06:32
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1016/j.proenv.2015.10.055
URI: http://psasir.upm.edu.my/id/eprint/42919
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