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Bayesian extreme modeling for non-stationary air quality data


Mohd Amin, Nor Azrita and Adam, Mohd Bakri and Ibrahim, Noor Akma and Aris, Ahmad Zaharin (2013) Bayesian extreme modeling for non-stationary air quality data. In: International Conference on Mathematical Sciences and Statistics 2013 (ICMSS2013), 5-7 Feb. 2013, Kuala Lumpur, Malaysia. (pp. 424-428).


The aim of this paper is to model the non-stationary Generalized Extreme Value distribution with a focus on Bayesian approach. The location parameter is expressed in terms of linear trend over the time period while constant for both scale and shape parameters. This study also explores the informative and Jeffrey's prior towards the efficiency of the estimating procedure. Root Mean Square Error is then use for choosing the best prior. Metropolis Hasting for extreme algorithm will also briefly explained in this study. The model is applied to the air quality data for Johor state.

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Additional Metadata

Item Type: Conference or Workshop Item (Paper)
Divisions: Faculty of Environmental Studies
Institute for Mathematical Research
DOI Number: https://doi.org/10.1063/1.4823949
Publisher: AIP Publishing LLC
Keywords: Air-pollution; Bayesian analysis; Generalized extreme value distribution; Metropolis-Hastings algorithm; Non-stationary
Depositing User: Nabilah Mustapa
Date Deposited: 08 Sep 2017 10:29
Last Modified: 08 Sep 2017 10:29
Altmetrics: http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4823949
URI: http://psasir.upm.edu.my/id/eprint/57200
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