• 主题
高级搜索  >
历史搜索 清空历史
首页> 外文期刊>Statistics and computing >A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems
【24h】

A goodness-of-fit test for multivariate multiparameter copulas based on multiplier central limit theorems

机译：基于乘数中心极限定理的多元多参数copula的拟合优度检验

获取原文
获取原文并翻译 | 示例
           
页面导航
  • 摘要
  • 著录项
  • 相似文献
  • 相关主题

摘要

Recent large scale simulations indicate that a powerful goodness-of-fit test for copulas can be obtained from the process comparing the empirical copula with a parametric estimate of the copula derived under the null hypothesis. A first way to compute approximate p-values for statistics derived from this process consists of using the parametric bootstrap procedure recently thoroughly revisited by Genest and Remillard. Because it heavily relies on random number generation and estimation, the resulting goodness-of-fit test has a very high computational cost that can be regarded as an obstacle to its application as the sample size increases. An alternative approach proposed by the authors consists of using a multiplier procedure. The study of the finite-sample performance of the multiplier version of the goodness-of-fit test for bivariate one-parameter copulas showed that it provides a valid alternative to the parametric bootstrap-based test while being orders of magnitude faster. The aim of this work is to extend the multiplier approach to multivariate multiparameter copulas and study the finite-sample performance of the resulting test. Particular emphasis is put on elliptical copulas such as the normal and the t as these are flexible models in a multivariate setting. The implementation of the procedure for the latter copulas proves challenging and requires the extension of the Plackett formula for the t distribution to arbitrary dimension. Extensive Monte Carlo experiments, which could be carried out only because of the good computational properties of the multiplier approach, confirm in the multivariate multiparameter context the satisfactory behavior of the goodness-of-fit test.
机译：最近的大规模模拟表明，可以通过将经验copula与在无效假设下得出的copula的参数估计值进行比较的过程，获得强大的copula拟合优度检验。计算从该过程中得出的统计数据的近似p值的第一种方法是使用最近由Genest和Remillard重新研究的参数引导程序。由于拟合优度测试很大程度上依赖于随机数的生成和估计，因此，拟合优度测试的计算成本非常高，随着样本量的增加，该测试成本可能会成为其应用的障碍。作者提出的另一种方法是使用乘法程序。对二元一参数copula拟合优度检验的乘数形式的有限样本性能的研究表明，它可以替代基于参数bootstrap的检验，但速度要快几个数量级。这项工作的目的是将乘数方法扩展到多元多参数copula，并研究所得测试的有限样本性能。由于椭圆变量在多变量环境中是灵活的模型，因此特别要注意它们。后一系动词的程序的实施具有挑战性，并且需要将用于t分布的Plackett公式扩展到任意维度。仅由于乘法器方法具有良好的计算特性，才可以进行广泛的蒙特卡洛实验，这些实验在多元多参数上下文中确认了拟合优度检验的令人满意的性能。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱：kefu@zhangqiaokeyan.com

京公网安备：11010802029741号 ICP备案号：京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有

  • 客服微信

  • 服务号