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Bayesian extreme modeling for non-stationary air quality data

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 informativ...

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Main Authors: Mohd Amin, Nor Azrita, Adam, Mohd Bakri, Ibrahim, Noor Akma, Aris, Ahmad Zaharin
Format: Conference or Workshop Item
Language:English
Published: AIP Publishing LLC 2013
Online Access:http://psasir.upm.edu.my/id/eprint/57200/1/Bayesian%20extreme%20modeling%20for%20non-stationary%20air%20quality%20data.pdf
http://psasir.upm.edu.my/id/eprint/57200/
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spelling my.upm.eprints.572002017-09-08T10:29:48Z http://psasir.upm.edu.my/id/eprint/57200/ Bayesian extreme modeling for non-stationary air quality data Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma Aris, Ahmad Zaharin 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. AIP Publishing LLC 2013 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57200/1/Bayesian%20extreme%20modeling%20for%20non-stationary%20air%20quality%20data.pdf 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). 10.1063/1.4823949
institution Universiti Putra Malaysia
building UPM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Putra Malaysia
content_source UPM Institutional Repository
url_provider http://psasir.upm.edu.my/
language English
description 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.
format Conference or Workshop Item
author Mohd Amin, Nor Azrita
Adam, Mohd Bakri
Ibrahim, Noor Akma
Aris, Ahmad Zaharin
spellingShingle Mohd Amin, Nor Azrita
Adam, Mohd Bakri
Ibrahim, Noor Akma
Aris, Ahmad Zaharin
Bayesian extreme modeling for non-stationary air quality data
author_facet Mohd Amin, Nor Azrita
Adam, Mohd Bakri
Ibrahim, Noor Akma
Aris, Ahmad Zaharin
author_sort Mohd Amin, Nor Azrita
title Bayesian extreme modeling for non-stationary air quality data
title_short Bayesian extreme modeling for non-stationary air quality data
title_full Bayesian extreme modeling for non-stationary air quality data
title_fullStr Bayesian extreme modeling for non-stationary air quality data
title_full_unstemmed Bayesian extreme modeling for non-stationary air quality data
title_sort bayesian extreme modeling for non-stationary air quality data
publisher AIP Publishing LLC
publishDate 2013
url http://psasir.upm.edu.my/id/eprint/57200/1/Bayesian%20extreme%20modeling%20for%20non-stationary%20air%20quality%20data.pdf
http://psasir.upm.edu.my/id/eprint/57200/
_version_ 1643836415656591360
score 13.244367

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