Nonparametric estimation of multivariate multiparameter conditional copulas

被引:2
|
作者
Lin, Jin-Guan [1 ]
Zhang, Kong-Sheng [2 ]
Zhao, Yan-Yong [1 ]
机构
[1] Nanjing Audit Univ, Dept Stat, Nanjing 211815, Jiangsu, Peoples R China
[2] Southeast Univ, Dept Math, Nanjing 211189, Jiangsu, Peoples R China
基金
高等学校博士学科点专项科研基金; 中国国家自然科学基金;
关键词
Conditional copula; Calibration function; Local linear smoothing; Newton-Raphson method; DEPENDENCE; MODELS;
D O I
10.1016/j.jkss.2016.08.003
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
Nonparametric estimation of conditional copulas with one parameter has been investigated in Acar et al. (2011). The estimation for multivariate multiparameter conditional copulas, however, has not been considered so far. This paper adopts the local linear smoothing technique and Newton-Raphson method to estimate those copulas. Under some regularity conditions, the asymptotic normality of the estimators is obtained. Simulation work shows the efficiency of the proposed method. As an application, we analyze a life expectancies data set and show that the conditional t copula outperforms the conditional Clayton, Frank and Gumbel copulas. (C) 2016 The Korean Statistical Society. Published by Elsevier B.V. All rights reserved.
引用
收藏
页码:126 / 136
页数:11
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