Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis

Analyzing extreme rainfall events is important as the results found can be advantageous for civil engineers and town planners to estimate the strength of building under the most extreme conditions. The annual maximum series (AMS) data of daily rainfall in Malacca were fitted to two distributions, na...

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Main Author:	Lim, Qi Su
Format:	Thesis
Language:	English
Published:	2014
Subjects:	QA Mathematics
Online Access:	http://eprints.utm.my/id/eprint/78744/1/LimQiSuMFS2014.pdf
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spelling	my-utm-ep.787442018-08-30T08:06:53Z Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis 2014-06 Lim, Qi Su QA Mathematics Analyzing extreme rainfall events is important as the results found can be advantageous for civil engineers and town planners to estimate the strength of building under the most extreme conditions. The annual maximum series (AMS) data of daily rainfall in Malacca were fitted to two distributions, namely Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution, by using Method of Moments (MOM), Maximum Likelihood Estimator (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. Previous studies have shown that the performance of the parameter estimations by using MOM was better than MLE. However, some researchers acknowledged that the parameter estimations of MLE can be additionally improved and developed by using Bayesian MCMC. To get posterior densities, non-informative and independent priors were used. The performance of the results were tested by several goodness of fit (GOF) tests. The results showed that GEV distribution is a better distribution than GP distribution in estimating the parameters of extreme daily rainfall amount in Malacca. MOM had maximum value return level for 10, 25, 50 and 100 years for most of stations followed by Bayesian MCMC. Conclusively, the results showed that Bayesian MCMC and MLE were equally better than MOM in estimating GEV and GP parameters. 2014-06 Thesis http://eprints.utm.my/id/eprint/78744/ http://eprints.utm.my/id/eprint/78744/1/LimQiSuMFS2014.pdf application/pdf en public http://dms.library.utm.my:8080/vital/access/manager/Repository/vital:105818 masters Universiti Teknologi Malaysia, Faculty of Science Faculty of Science
institution	Universiti Teknologi Malaysia
collection	UTM Institutional Repository
language	English
topic	QA Mathematics
spellingShingle	QA Mathematics Lim, Qi Su Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
description	Analyzing extreme rainfall events is important as the results found can be advantageous for civil engineers and town planners to estimate the strength of building under the most extreme conditions. The annual maximum series (AMS) data of daily rainfall in Malacca were fitted to two distributions, namely Generalized Extreme Value (GEV) and Generalized Pareto (GP) distribution, by using Method of Moments (MOM), Maximum Likelihood Estimator (MLE) and Bayesian Markov Chain Monte Carlo (MCMC) simulations. Previous studies have shown that the performance of the parameter estimations by using MOM was better than MLE. However, some researchers acknowledged that the parameter estimations of MLE can be additionally improved and developed by using Bayesian MCMC. To get posterior densities, non-informative and independent priors were used. The performance of the results were tested by several goodness of fit (GOF) tests. The results showed that GEV distribution is a better distribution than GP distribution in estimating the parameters of extreme daily rainfall amount in Malacca. MOM had maximum value return level for 10, 25, 50 and 100 years for most of stations followed by Bayesian MCMC. Conclusively, the results showed that Bayesian MCMC and MLE were equally better than MOM in estimating GEV and GP parameters.
format	Thesis
qualification_level	Master's degree
author	Lim, Qi Su
author_facet	Lim, Qi Su
author_sort	Lim, Qi Su
title	Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
title_short	Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
title_full	Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
title_fullStr	Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
title_full_unstemmed	Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
title_sort	parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis
granting_institution	Universiti Teknologi Malaysia, Faculty of Science
granting_department	Faculty of Science
publishDate	2014
url	http://eprints.utm.my/id/eprint/78744/1/LimQiSuMFS2014.pdf
_version_	1747818060666896384

Parameter estimation for generalized extreme value and generalized pareto in extreme rainfall analysis

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