Bayesian extreme for modeling high PM10 concentration in Johor
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 genera...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2015
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| Online Access: | http://psasir.upm.edu.my/id/eprint/42919/1/42919.pdf |
| _version_ | 1796974239295733760 |
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| author | Mohd Amin, Nor Azrita Adam, Mohd Bakri Aris, Ahmad Zaharin |
| author_facet | Mohd Amin, Nor Azrita Adam, Mohd Bakri Aris, Ahmad Zaharin |
| author_sort | Mohd Amin, Nor Azrita |
| collection | UPM |
| description | 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. |
| first_indexed | 2024-03-06T08:54:10Z |
| format | Article |
| id | upm.eprints-42919 |
| institution | Universiti Putra Malaysia |
| language | English |
| last_indexed | 2024-03-06T08:54:10Z |
| publishDate | 2015 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | upm.eprints-429192016-05-03T06:32:26Z http://psasir.upm.edu.my/id/eprint/42919/ Bayesian extreme for modeling high PM10 concentration in Johor Mohd Amin, Nor Azrita Adam, Mohd Bakri Aris, Ahmad Zaharin 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. Elsevier 2015 Article PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/42919/1/42919.pdf 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 http://www.sciencedirect.com/science/article/pii/S1878029615006490 10.1016/j.proenv.2015.10.055 |
| spellingShingle | Mohd Amin, Nor Azrita Adam, Mohd Bakri Aris, Ahmad Zaharin Bayesian extreme for modeling high PM10 concentration in Johor |
| title | Bayesian extreme for modeling high PM10 concentration in Johor |
| title_full | Bayesian extreme for modeling high PM10 concentration in Johor |
| title_fullStr | Bayesian extreme for modeling high PM10 concentration in Johor |
| title_full_unstemmed | Bayesian extreme for modeling high PM10 concentration in Johor |
| title_short | Bayesian extreme for modeling high PM10 concentration in Johor |
| title_sort | bayesian extreme for modeling high pm10 concentration in johor |
| url | http://psasir.upm.edu.my/id/eprint/42919/1/42919.pdf |
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