Bayesian inference for extreme quantiles of heavy tailed distributions

Farias, Rafael B. A.; Montoril, Michel H.; Andrade, Jose A. A.

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(Reference retrieved automatically from Web of Science through information on FAPESP grant and its corresponding number as mentioned in the publication by the authors.)

Bayesian inference for extreme quantiles of heavy tailed distributions

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Author(s):	Farias, Rafael B. A. ^[1] ; Montoril, Michel H. ^[2] ; Andrade, Jose A. A. ^[1] Total Authors: 3
Affiliation:	^[1] Univ Fed Ceara, Dept Stat & Appl Math, Fortaleza, Ceara - Brazil ^[2] Univ Fed Juiz de Fora, Dept Stat, Juiz De Fora - Brazil Total Affiliations: 2
Document type:	Journal article
Source:	Statistics & Probability Letters; v. 113, p. 103-107, JUN 2016.
Web of Science Citations:	2
Abstract
We propose a new method for estimating extremes quantiles of a wide class of heavy-tailed distributions. Our proposal makes Bayesian inference on extreme quantiles through High Posterior Density intervals. We evaluate the performance of the proposal by numerical results. (C) 2016 Elsevier B.V. All rights reserved. (AU)

FAPESP's process:	13/09035-1 - Regression models in functional data analysis
Grantee:	Michel Helcias Montoril
Support Opportunities:	Scholarships in Brazil - Post-Doctoral

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