Yuzhi Cai
Minimum sample size determination for generalized extreme value distribution
Cai, Yuzhi; Hames, Dominic
Authors
Dominic Hames
Abstract
Sample size determination is an important issue in statistical analysis. Obviously, the larger the sample size is, the better the statistical results we have. However, in many areas such as coastal engineering and environmental sciences, it can be very expensive or even impossible to collect large samples. In this article, we propose a general method for determining the minimum sample size required by estimating the return levels from a generalized extreme value distribution. Both simulation studies and the applications to real data sets show that the method is easy to implement and the results obtained are very good.
Journal Article Type | Article |
---|---|
Acceptance Date | Sep 28, 2010 |
Online Publication Date | Jan 20, 2011 |
Publication Date | 2010 |
Deposit Date | Sep 12, 2023 |
Journal | Communications in Statistics: Simulation and Computation |
Print ISSN | 0361-0918 |
Electronic ISSN | 1532-4141 |
Publisher | Taylor & Francis |
Peer Reviewed | Peer Reviewed |
Volume | 40 |
Issue | 1 |
Pages | 87-98 |
DOI | https://doi.org/10.1080/03610918.2010.530368 |
Public URL | https://uwe-repository.worktribe.com/output/11104117 |
Publisher URL | https://www.tandfonline.com/toc/lssp20/40/1?nav=tocList |
You might also like
Impacts of sea level rise on wave overtopping rates around the coast of England
(2022)
Journal Article
Effect of sea level rise on the overtopping of English coastal defences
(2021)
Presentation / Conference Contribution
Investigating the use of joint probability curves in coastal engineering practice
(2020)
Journal Article
Change risk assessment for Scotland
(2012)
Report
Downloadable Citations
About UWE Bristol Research Repository
Administrator e-mail: repository@uwe.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2024
Advanced Search