Cover Image

Bivariate generalized Pareto distribution for extreme atmospheric particulate matter

The high particulate matter (PM10) level is the prominent issue causing various impacts to human health and seriously affecting the economics. The asymptotic theory of extreme value is apply for analyzing the relation of extreme PM10 data from two nearby air quality monitoring stations. The series o...

Full description

Saved in:
Bibliographic Details
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 2014
Online Access:http://psasir.upm.edu.my/id/eprint/57546/1/Bivariate%20generalized%20Pareto%20distribution%20for%20extreme%20atmospheric%20particulate%20matter.pdf
http://psasir.upm.edu.my/id/eprint/57546/
Tags: Add Tag
No Tags, Be the first to tag this record!
id my.upm.eprints.57546
record_format eprints
spelling my.upm.eprints.575462017-10-24T04:06:50Z http://psasir.upm.edu.my/id/eprint/57546/ Bivariate generalized Pareto distribution for extreme atmospheric particulate matter Mohd Amin, Nor Azrita Adam, Mohd Bakri Ibrahim, Noor Akma Aris, Ahmad Zaharin The high particulate matter (PM10) level is the prominent issue causing various impacts to human health and seriously affecting the economics. The asymptotic theory of extreme value is apply for analyzing the relation of extreme PM10 data from two nearby air quality monitoring stations. The series of daily maxima PM10 for Johor Bahru and Pasir Gudang stations are consider for year 2001 to 2010 databases. The 85% and 95% marginal quantile apply to determine the threshold values and hence construct the series of exceedances over the chosen threshold. The logistic, asymmetric logistic, negative logistic and asymmetric negative logistic models areconsidered as the dependence function to the joint distribution of a bivariate observation. Maximum likelihood estimation is employed for parameter estimations. The best fitted model is chosen based on the Akaike Information Criterion and the quantile plots. It is found that the asymmetric logistic model gives the best fitted model for bivariate extreme PM10 data and shows the weak dependence between two stations. AIP Publishing LLC 2014 Conference or Workshop Item PeerReviewed application/pdf en http://psasir.upm.edu.my/id/eprint/57546/1/Bivariate%20generalized%20Pareto%20distribution%20for%20extreme%20atmospheric%20particulate%20matter.pdf Mohd Amin, Nor Azrita and Adam, Mohd Bakri and Ibrahim, Noor Akma and Aris, Ahmad Zaharin (2014) Bivariate generalized Pareto distribution for extreme atmospheric particulate matter. In: 2nd ISM International Statistical Conference 2014 (ISM-II), 12-14 Aug. 2014, MS Garden Hotel, Kuantan, Pahang. (pp. 201-205). 10.1063/1.4907445
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 high particulate matter (PM10) level is the prominent issue causing various impacts to human health and seriously affecting the economics. The asymptotic theory of extreme value is apply for analyzing the relation of extreme PM10 data from two nearby air quality monitoring stations. The series of daily maxima PM10 for Johor Bahru and Pasir Gudang stations are consider for year 2001 to 2010 databases. The 85% and 95% marginal quantile apply to determine the threshold values and hence construct the series of exceedances over the chosen threshold. The logistic, asymmetric logistic, negative logistic and asymmetric negative logistic models areconsidered as the dependence function to the joint distribution of a bivariate observation. Maximum likelihood estimation is employed for parameter estimations. The best fitted model is chosen based on the Akaike Information Criterion and the quantile plots. It is found that the asymmetric logistic model gives the best fitted model for bivariate extreme PM10 data and shows the weak dependence between two stations.
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
Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
author_facet Mohd Amin, Nor Azrita
Adam, Mohd Bakri
Ibrahim, Noor Akma
Aris, Ahmad Zaharin
author_sort Mohd Amin, Nor Azrita
title Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
title_short Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
title_full Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
title_fullStr Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
title_full_unstemmed Bivariate generalized Pareto distribution for extreme atmospheric particulate matter
title_sort bivariate generalized pareto distribution for extreme atmospheric particulate matter
publisher AIP Publishing LLC
publishDate 2014
url http://psasir.upm.edu.my/id/eprint/57546/1/Bivariate%20generalized%20Pareto%20distribution%20for%20extreme%20atmospheric%20particulate%20matter.pdf
http://psasir.upm.edu.my/id/eprint/57546/
_version_ 1643836519726710784
score 13.211869

Similar Items