The dependency effect to extreme value distributions (EVDs) using the frequentist and Bayesian approaches have been used to analyse the extremes of annual and daily maximum wind speed at Port Elizabeth, South Africa. In the frequentist approach, the parameters of EVDs were estimated using maximum likelihood, whereas in the Bayesian approach the Markov Chain Monte Carlo technique with the Metropolis–Hastings algorithm was used. The results show that the EVDs fitted considering the dependency and seasonality effects with in the data series provide apparent benefits in terms of improved precision in estimation of the parameters as well as return levels of the distributions. The paper also discusses a method to construct informative priors empirically using historical data of the underlying process from other weather stations. The results from the Bayesian analysis show that posterior inference might be affected by the choice of priors used to formulate the informative priors. The Bayesian approach provides satisfactory estimation strategy in terms of precision compared to the frequentist approach, accounting for uncertainty in parameters and return levels estimation.

中文翻译：

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对南非伊丽莎白港极端风速的依赖效应对极值分布的影响建模：一种频繁和贝叶斯方法

使用频繁和贝叶斯方法对极值分布（EVD）的依赖效应已用于分析南非伊丽莎白港的年和日最大风速极值。在常客方法中，EVD的参数是使用最大似然估计的，而在贝叶斯方法中，使用了带有Metropolis-Hastings算法的马尔可夫链蒙特卡罗技术。结果表明，考虑数据序列中的依赖性和季节性影响而拟合的EVD在提高参数估计精度以及分布的返回水平方面提供了明显的好处。本文还讨论了一种使用其他气象站的基础过程的历史数据凭经验构建信息先验的方法。贝叶斯分析的结果表明，后验推论可能会受到用于形成信息先验的先验选择的影响。与频繁方法相比，贝叶斯方法在准确性方面提供了令人满意的估计策略，考虑了参数的不确定性和收益水平的估计。