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Minimum sample size determination for generalized extreme value distribution.

Yuzhi Cai Orcid Logo, Dominic Hames

Communications in Statistics – Simulation and Computation, Volume: 40, Pages: 99 - 110

Swansea University Author: Yuzhi Cai Orcid Logo

Abstract

Sample size determination is an important issue in statistical analysis. Obviously, thelarger the sample size is, the better the statistical results we have. However, in manyareas such as coastal engineering and environmental sciences, it can be veryexpensive or even impossible to collect large samp...

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Published in: Communications in Statistics – Simulation and Computation
Published: 2011
URI: https://cronfa.swan.ac.uk/Record/cronfa11974
first_indexed 2013-07-23T12:06:38Z
last_indexed 2018-02-09T04:41:55Z
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spelling 2011-10-01T00:00:00.0000000 v2 11974 2012-07-12 Minimum sample size determination for generalized extreme value distribution. eff7b8626ab4cc6428eef52516fda7d6 0000-0003-3509-9787 Yuzhi Cai Yuzhi Cai true false 2012-07-12 CBAE Sample size determination is an important issue in statistical analysis. Obviously, thelarger the sample size is, the better the statistical results we have. However, in manyareas such as coastal engineering and environmental sciences, it can be veryexpensive or even impossible to collect large samples. In this paper, we propose ageneral method for determining the minimum sample size required by estimating thereturn levels from a generalized extreme value distribution. Both simulation studiesand the applications to real data sets show that the method is easy to implementand the results obtained are very good. Journal Article Communications in Statistics – Simulation and Computation 40 99 110 Bootstraping; Generalized extreme value distribution; Return level; Sample size. 31 1 2011 2011-01-31 COLLEGE NANME Management School COLLEGE CODE CBAE Swansea University 2011-10-01T00:00:00.0000000 2012-07-12T13:53:18.5693416 Faculty of Humanities and Social Sciences School of Management - Accounting and Finance Yuzhi Cai 0000-0003-3509-9787 1 Dominic Hames 2
title Minimum sample size determination for generalized extreme value distribution.
spellingShingle Minimum sample size determination for generalized extreme value distribution.
Yuzhi Cai
title_short Minimum sample size determination for generalized extreme value distribution.
title_full Minimum sample size determination for generalized extreme value distribution.
title_fullStr Minimum sample size determination for generalized extreme value distribution.
title_full_unstemmed Minimum sample size determination for generalized extreme value distribution.
title_sort Minimum sample size determination for generalized extreme value distribution.
author_id_str_mv eff7b8626ab4cc6428eef52516fda7d6
author_id_fullname_str_mv eff7b8626ab4cc6428eef52516fda7d6_***_Yuzhi Cai
author Yuzhi Cai
author2 Yuzhi Cai
Dominic Hames
format Journal article
container_title Communications in Statistics – Simulation and Computation
container_volume 40
container_start_page 99
publishDate 2011
institution Swansea University
college_str Faculty of Humanities and Social Sciences
hierarchytype
hierarchy_top_id facultyofhumanitiesandsocialsciences
hierarchy_top_title Faculty of Humanities and Social Sciences
hierarchy_parent_id facultyofhumanitiesandsocialsciences
hierarchy_parent_title Faculty of Humanities and Social Sciences
department_str School of Management - Accounting and Finance{{{_:::_}}}Faculty of Humanities and Social Sciences{{{_:::_}}}School of Management - Accounting and Finance
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description Sample size determination is an important issue in statistical analysis. Obviously, thelarger the sample size is, the better the statistical results we have. However, in manyareas such as coastal engineering and environmental sciences, it can be veryexpensive or even impossible to collect large samples. In this paper, we propose ageneral method for determining the minimum sample size required by estimating thereturn levels from a generalized extreme value distribution. Both simulation studiesand the applications to real data sets show that the method is easy to implementand the results obtained are very good.
published_date 2011-01-31T10:37:53Z
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score 11.08899

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