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首页> 外文会议>International FLINS conference >ANALYSIS OF THE IMPACTS OF THE DATA LENGTH ON THE UNCERTAINTY OF THE ARCHIMEDEAN COPULA MODELING
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ANALYSIS OF THE IMPACTS OF THE DATA LENGTH ON THE UNCERTAINTY OF THE ARCHIMEDEAN COPULA MODELING

机译：数据长度对Archimedean Copula建模不确定性的影响分析

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Copulas have been frequently used in solving issues about modeling multivariate variables for the significant advantage that it can capture the multivariate dependence of the variables irrespective of their marginal distributions. The parametric families of Archimedean copulas are widely used in hydrologic frequency analysis due to their simple and closed form expressions. This paper mainly deals with the impacts of the length of hydrological data on the uncertainty of the modeling results of the Archimedean copulas. Several estimation methods are implemented including the inference from marginals (IFM) method, canonical maximum likelihood estimator (CMLE) and the estimation based on Kendall's tau. The results show that the length of data set will affect the choice of the best fitted copula and it is more reliable to use CMLE and estimation via Kendall' tau when the length of the data set changes.
机译：Copulas已被广泛用于解决有关对多元变量建模的问题，其显着优点是，无论变量的边际分布如何，它都能捕获变量的多元依赖性。阿基米德系系的参数族由于其简单和封闭的形式表达而广泛用于水文频率分析。本文主要研究水文数据长度对阿基米德河系建模结果不确定性的影响。实现了几种估计方法，包括边际推断（IFM）方法，规范最大似然估计器（CMLE）和基于Kendall tau的估计。结果表明，数据集的长度将影响最佳拟合系的选择，并且当数据集的长度发生变化时，使用CMLE和通过Kendall'tau进行估计更为可靠。

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