Lillo, C, Leiva, V, Nicolis, O et al. (1 more author) (2018) L-moments of the Birnbaum-Saunders distribution and its extreme value version: Estimation, goodness of fit and application to earthquake data. Journal of Applied Statistics, 45 (2). pp. 187-209. ISSN 0266-4763
Abstract
Understanding patterns in the frequency of extreme natural events, such as earthquakes, is important as it helps in the prediction of their future occurrence and hence provides better civil protection. Distributions describing these events are known to be heavy tailed and positive skew making standard distributions unsuitable for such a situation. The Birnbaum-Saunders distribution and its extreme value version have been widely studied and applied due to their attractive properties. We derive L-moment equations for these distributions and propose novel methods for parameter estimation, goodness-of-fit assessment and model selection. A simulation study is conducted to evaluate the performance of the L-moment estimators, which is compared to that of the maximum likelihood estimators, demonstrating the superiority of the proposed methods. To illustrate these methods in a practical application, a data analysis of real-world earthquake magnitudes, obtained from the global centroid moment tensor catalogue during 1962-2015, is carried out. This application identifies the extreme value Birnbaum-Saunders distribution as a better model than classic extreme value distributions for describing seismic events.
Metadata
Item Type: | Article |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2016 Informa UK Limited, trading as Taylor & Francis Group. This is an Accepted Manuscript of an article published by Taylor & Francis in Journal of Applied Statistics on 30th December 2016, available online: http://www.tandfonline.com/10.1080/02664763.2016.1269729 |
Keywords: | GCMT catalogue; Generalized extreme value distributions; goodness-of-fit methods; maximum likelihood and moment estimation; Monte-Carlo simulation; R software |
Dates: |
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Institution: | The University of Leeds |
Academic Units: | The University of Leeds > Faculty of Engineering & Physical Sciences (Leeds) > School of Mathematics (Leeds) > Statistics (Leeds) |
Depositing User: | Symplectic Publications |
Date Deposited: | 22 Nov 2016 10:36 |
Last Modified: | 30 Dec 2017 01:38 |
Published Version: | https://doi.org/10.1080/02664763.2016.1269729 |
Status: | Published |
Publisher: | Taylor & Francis |
Identification Number: | 10.1080/02664763.2016.1269729 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:107937 |