Multiple-try Metropolis Hastings for modeling extreme PM10 data
Awareness of catastrophic events brings the attention to work out the relationship of these events by using statistical analysis of Extreme Value Theory (EVT). This study focused on extreme PM10 data using a Gumbel distribution which is one of the Extreme Value distributions. The parameters were est...
| Main Authors: | , , |
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| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
AIP Publishing LLC
2013
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| Online Access: | http://psasir.upm.edu.my/id/eprint/57434/ http://psasir.upm.edu.my/id/eprint/57434/1/Multiple-try%20Metropolis%20Hastings%20for%20modeling%20extreme%20PM10%20data.pdf |
| _version_ | 1848853365117681664 |
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| author | Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma |
| author_facet | Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma |
| author_sort | Mohd Amin, Nor Azrita |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Awareness of catastrophic events brings the attention to work out the relationship of these events by using statistical analysis of Extreme Value Theory (EVT). This study focused on extreme PM10 data using a Gumbel distribution which is one of the Extreme Value distributions. The parameters were estimated using the new Bayesian approach in extreme called Multiple Try Metropolis-Hastings algorithms. We compared this approach with another Markov Chain Monte Carlo approach which is the classical Metropolis-Hastings algorithm and the frequentist approach, Maximum Likelihood Estimation. It appears that these three approaches provide comparable results. Data are taken for Pasir Gudang station for year 1996 to 2010. |
| first_indexed | 2025-11-15T10:52:49Z |
| format | Conference or Workshop Item |
| id | upm-57434 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T10:52:49Z |
| publishDate | 2013 |
| publisher | AIP Publishing LLC |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-574342017-09-27T10:19:14Z http://psasir.upm.edu.my/id/eprint/57434/ Multiple-try Metropolis Hastings for modeling extreme PM10 data Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma Awareness of catastrophic events brings the attention to work out the relationship of these events by using statistical analysis of Extreme Value Theory (EVT). This study focused on extreme PM10 data using a Gumbel distribution which is one of the Extreme Value distributions. The parameters were estimated using the new Bayesian approach in extreme called Multiple Try Metropolis-Hastings algorithms. We compared this approach with another Markov Chain Monte Carlo approach which is the classical Metropolis-Hastings algorithm and the frequentist approach, Maximum Likelihood Estimation. It appears that these three approaches provide comparable results. Data are taken for Pasir Gudang station for year 1996 to 2010. AIP Publishing LLC 2013 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57434/1/Multiple-try%20Metropolis%20Hastings%20for%20modeling%20extreme%20PM10%20data.pdf Mohd Amin, Nor Azrita and Adam, Mohd Bakri and Ibrahim, Noor Akma (2013) Multiple-try Metropolis Hastings for modeling extreme PM10 data. In: 21st National Symposium on Mathematical Sciences (SKSM21), 6-8 Nov. 2013, Penang, Malaysia. (pp. 949-954). 10.1063/1.4887718 |
| spellingShingle | Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title | Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title_full | Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title_fullStr | Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title_full_unstemmed | Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title_short | Multiple-try Metropolis Hastings for modeling extreme PM10 data |
| title_sort | multiple-try metropolis hastings for modeling extreme pm10 data |
| url | http://psasir.upm.edu.my/id/eprint/57434/ http://psasir.upm.edu.my/id/eprint/57434/ http://psasir.upm.edu.my/id/eprint/57434/1/Multiple-try%20Metropolis%20Hastings%20for%20modeling%20extreme%20PM10%20data.pdf |