On Bayesian approach to composite Pareto models.

In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to t...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Muhammad Hilmi Abdul Majid, Kamarulzaman Ibrahim
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/139d9d42518e42c48d4d3fd8fd714026
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:139d9d42518e42c48d4d3fd8fd714026
record_format	dspace
spelling	oai:doaj.org-article:139d9d42518e42c48d4d3fd8fd7140262021-12-02T20:14:12ZOn Bayesian approach to composite Pareto models.1932-620310.1371/journal.pone.0257762https://doaj.org/article/139d9d42518e42c48d4d3fd8fd7140262021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0257762https://doaj.org/toc/1932-6203In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.Muhammad Hilmi Abdul MajidKamarulzaman IbrahimPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 9, p e0257762 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Muhammad Hilmi Abdul Majid Kamarulzaman Ibrahim On Bayesian approach to composite Pareto models.
description	In data modelling using the composite Pareto distribution, any observations above a particular threshold value are assumed to follow Pareto type distribution, whereas the rest of the observations are assumed to follow a different distribution. This paper proposes on the use of Bayesian approach to the composite Pareto models involving specification of the prior distribution on the proportion of data coming from the Pareto distribution, instead of assuming the prior distribution on the threshold, as often done in the literature. Based on a simulation study, it is found that the parameter estimates determined when using uniform prior on the proportion is less biased as compared to the point estimates determined when using uniform prior on the threshold. Applications on income data and finance are included for illustrative examples.
format	article
author	Muhammad Hilmi Abdul Majid Kamarulzaman Ibrahim
author_facet	Muhammad Hilmi Abdul Majid Kamarulzaman Ibrahim
author_sort	Muhammad Hilmi Abdul Majid
title	On Bayesian approach to composite Pareto models.
title_short	On Bayesian approach to composite Pareto models.
title_full	On Bayesian approach to composite Pareto models.
title_fullStr	On Bayesian approach to composite Pareto models.
title_full_unstemmed	On Bayesian approach to composite Pareto models.
title_sort	on bayesian approach to composite pareto models.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/139d9d42518e42c48d4d3fd8fd714026
work_keys_str_mv	AT muhammadhilmiabdulmajid onbayesianapproachtocompositeparetomodels AT kamarulzamanibrahim onbayesianapproachtocompositeparetomodels
_version_	1718374688599572480

On Bayesian approach to composite Pareto models.

Ejemplares similares