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Minimum Sample Size Determination for Generalized Extreme Value Distribution

机译:通用极值分布的最小样本量确定

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摘要

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.
机译:样本量的确定是统计分析中的重要问题。显然,样本量越大,我们的统计结果越好。但是,在沿海工程和环境科学等许多领域,收集大样本可能非常昂贵,甚至不可能。在本文中,我们提出了一种通过从广义极值分布中估算回报水平来确定所需最小样本量的通用方法。仿真研究和对实际数据集的应用都表明该方法易于实现,并且获得的结果非常好。

著录项

  • 来源
  • 作者

    Yuzhi Cai;

  • 作者单位

    School of Computing and Mathematics, University of Plymouth, Plymouth, United Kingdom;

    HR Wallingford, Oxfordshire, United Kingdom;

  • 收录信息 美国《科学引文索引》(SCI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

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