Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data

Modelling the tails of distributions is important in many areas of research where the risk of unusually small or large events are of interest. In this research, application of extreme value theory within a Bayesian framework using the Metropolis Hastings algorithm and the slice sampler algorithm...

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Main Author:	Rostami, Mohammad
Format:	Thesis
Language:	English
Published:	2016
Subjects:	Bayesian statistical decision theory Algorithms
Online Access:	http://psasir.upm.edu.my/id/eprint/69793/1/IPM%202016%2010%20-%20IR.pdf
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id	my-upm-ir.69793
record_format	uketd_dc
spelling	my-upm-ir.697932019-10-29T07:00:35Z Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data 2016-02 Rostami, Mohammad Modelling the tails of distributions is important in many areas of research where the risk of unusually small or large events are of interest. In this research, application of extreme value theory within a Bayesian framework using the Metropolis Hastings algorithm and the slice sampler algorithm as an alternative approach, has been introduced. Selection of prior distributions are very crucial in Bayesian analysis. Here, we have exhaustedly studied all the possible priors for location and scale parameters and come out with a few suggestions for the prior selection of a Gumbel model. The slice sampler method can adaptively change the scale of changes made, which makes it easier to tune than Metropolis Hastings algorithm. Another important benefit of the slice sampler algorithm is that it provides posterior means with low errors for the shape parameters of the monthly maximum and threshold exceedances models. The slice sampler algorithm has been extended for more complex bivariate extreme value model with logistic dependence structure and exponential margins. A simulation study shows that the slice sampler algorithm provides posterior means with low errors for the parameters along with a high level of stationarity in iteration series. Furthermore, the slice sampler algorithm has been successfully applied to Malaysian gold returns which has been calculated using Malaysian daily gold prices from 2000 to 2011. By using a Bivariate extreme model and the slice sampler algorithm, the relationship between the gold and American dollar returns in Malaysian market has been considered. Bayesian statistical decision theory Algorithms 2016-02 Thesis http://psasir.upm.edu.my/id/eprint/69793/ http://psasir.upm.edu.my/id/eprint/69793/1/IPM%202016%2010%20-%20IR.pdf text en public doctoral Universiti Putra Malaysia Bayesian statistical decision theory Algorithms
institution	Universiti Putra Malaysia
collection	PSAS Institutional Repository
language	English
topic	Bayesian statistical decision theory Algorithms
spellingShingle	Bayesian statistical decision theory Algorithms Rostami, Mohammad Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
description	Modelling the tails of distributions is important in many areas of research where the risk of unusually small or large events are of interest. In this research, application of extreme value theory within a Bayesian framework using the Metropolis Hastings algorithm and the slice sampler algorithm as an alternative approach, has been introduced. Selection of prior distributions are very crucial in Bayesian analysis. Here, we have exhaustedly studied all the possible priors for location and scale parameters and come out with a few suggestions for the prior selection of a Gumbel model. The slice sampler method can adaptively change the scale of changes made, which makes it easier to tune than Metropolis Hastings algorithm. Another important benefit of the slice sampler algorithm is that it provides posterior means with low errors for the shape parameters of the monthly maximum and threshold exceedances models. The slice sampler algorithm has been extended for more complex bivariate extreme value model with logistic dependence structure and exponential margins. A simulation study shows that the slice sampler algorithm provides posterior means with low errors for the parameters along with a high level of stationarity in iteration series. Furthermore, the slice sampler algorithm has been successfully applied to Malaysian gold returns which has been calculated using Malaysian daily gold prices from 2000 to 2011. By using a Bivariate extreme model and the slice sampler algorithm, the relationship between the gold and American dollar returns in Malaysian market has been considered.
format	Thesis
qualification_level	Doctorate
author	Rostami, Mohammad
author_facet	Rostami, Mohammad
author_sort	Rostami, Mohammad
title	Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
title_short	Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
title_full	Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
title_fullStr	Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
title_full_unstemmed	Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
title_sort	slice sampler and metropolis hastings approaches for bayesian analysis of extreme data
granting_institution	Universiti Putra Malaysia
publishDate	2016
url	http://psasir.upm.edu.my/id/eprint/69793/1/IPM%202016%2010%20-%20IR.pdf
_version_	1747812727523377152

Slice sampler and metropolis hastings approaches for bayesian analysis of extreme data

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