Semiparametric estimation in copula models
Semiparametric estimation in copula models
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DOI：
10.1002/cjs.5540330304
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发表时间：
2005-09-01
影响因子：
0.6
通讯作者：
Tsukahara, H
中科院分区：
文献类型：
--
作者：
Tsukahara, H
作者回顾了经验Copula过程的极限行为，并应用它证明了Copula模型中欧氏参数的最小距离估计的一些渐近性质。估计的问题是半参数的，没有知识的边际分布是必要的。作者还提出了另一种半参数估计，他称之为“秩近似Z-估计”，他推导出其渐近正态性。他进一步提出了Monte Carlo模拟结果比较各种估计在四个著名的二元copula模型。
The author recalls the limiting behaviour of the empirical copula process and applies it to prove some asymptotic properties of a minimum distance estimator for a Euclidean parameter in a copula model. The estimator in question is semiparametric in that no knowledge of the marginal distributions is necessary. The author also proposes another semiparametric estimator which he calls "rank approximate Z-estimator" and whose asymptotic normality he derives. He further presents Monte Carlo simulation results for the comparison of various estimators in four well-known bivariate copula models.