Multiple-try Metropolis Hastings for modeling extreme PM10 data

Citation

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).

Abstract

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.

Download File

Preview

PDF (Abstract)
Multiple-try Metropolis Hastings for modeling extreme PM10 data.pdf
Download (35kB) | Preview

Additional Metadata

Item Type:	Conference or Workshop Item (Paper)
Divisions:	Institute for Mathematical Research
DOI Number:	https://doi.org/10.1063/1.4887718
Publisher:	AIP Publishing LLC
Keywords:	Air quality; Gumbel distribution; Multiple-try Metropolis Hastings; Return value
Depositing User:	Nabilah Mustapa
Date Deposited:	27 Sep 2017 10:19
Last Modified:	27 Sep 2017 10:19
Altmetrics:	http://www.altmetric.com/details.php?domain=psasir.upm.edu.my&doi=10.1063/1.4887718
URI:	http://psasir.upm.edu.my/id/eprint/57434
Statistic Details:	View Download Statistic

Actions (login required)

View Item