On the estimation of nested Archimedean copulas: a theoretical and an experimental comparison

被引:0
|
作者
Nathan Uyttendaele
机构
[1] Université catholique de Louvain,Institut de Statistique, Biostatistique et Sciences Actuarielles
来源
Computational Statistics | 2018年 / 33卷
关键词
Hierarchical Archimedean copulas; Estimation; Hierarchical clustering; Rooted tree; Structure determination; Kendall’s tau; Phylogenetics;
D O I
暂无
中图分类号
学科分类号
摘要
A lot of progress regarding estimation of nested Archimedean copulas has been booked since their introduction by Joe (Multivariate models and dependence concepts. Chapman and Hall, London, 1997). The currently published procedures can be seen as particular cases of two different, more general, approaches. In the first approach, the tree structure of the target nested Archimedean copulas is estimated using hierarchical clustering to get a binary tree, and then parts of this binary tree are collapsed according to some strategy. This two-step estimation of the tree structure paves the way for estimation of the generators according to the sufficient nesting condition afterwards, this sufficient nesting condition on the generators ensuring the resulting estimated nested Archimedean copula is a proper copula. In contrast to the first approach, the second approach estimates the tree structure free of any concern for the generators. While this is the main strength of this second approach, it is also its main weakness: estimation of the generators afterwards so that the resulting nested Archimedean copula is a proper copula still lacks a solution. In this paper, both approaches are formally explored, detailed explanations and examples are given, as well as results from a performance study where a new way of comparing tree structure estimators is offered. A nested Archimedean copula is also estimated based on exams results from 482 students, and a naive attempt to check the fit is made using principal component analysis.
引用
收藏
页码:1047 / 1070
页数:23
相关论文
共 50 条
  • [41] ARCHIMEDEAN COPULAS AND TEMPORAL DEPENDENCE
    Beare, Brendan K.
    [J]. ECONOMETRIC THEORY, 2012, 28 (06) : 1165 - 1185
  • [42] Simulation for Mixture of Archimedean Copulas
    Ou, Shide
    [J]. MECHANICAL ENGINEERING AND INTELLIGENT SYSTEMS, PTS 1 AND 2, 2012, 195-196 : 738 - 743
  • [43] On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators
    Di Bernardino, Elena
    Rulliere, Didier
    [J]. DEPENDENCE MODELING, 2013, 1 (01): : 1 - 36
  • [44] Multiobjective Estimation of Distribution Algorithms Using Multivariate Archimedean Copulas and Average Ranking
    Gao, Ying
    Peng, Lingxi
    Li, Fufang
    Liu, Miao
    Hu, Xiao
    [J]. FOUNDATIONS OF INTELLIGENT SYSTEMS (ISKE 2013), 2014, 277 : 591 - 601
  • [45] Bivariate empirical and n-variate Archimedean copulas in Estimation of Distribution Algorithms
    Cuesta-Infante, Alfredo
    Santana, Roberto
    Hidalgo, J. Ignacio
    Bielza, Concha
    Larranaga, Pedro
    [J]. 2010 IEEE CONGRESS ON EVOLUTIONARY COMPUTATION (CEC), 2010,
  • [46] Structure Determination and Estimation of Hierarchical Archimedean Copulas Based on Kendall Correlation Matrix
    Gorecki, Jan
    Holena, Martin
    [J]. NEW FRONTIERS IN MINING COMPLEX PATTERNS, NFMCP 2013, 2014, 8399 : 132 - 147
  • [47] Tails of multivariate Archimedean copulas.
    Charpentier, Arthur
    Segers, Johan
    [J]. INSURANCE MATHEMATICS & ECONOMICS, 2006, 39 (03): : 412 - 412
  • [48] Exact simulation of reciprocal Archimedean copulas
    Mai, Jan-Frederik
    [J]. STATISTICS & PROBABILITY LETTERS, 2018, 141 : 68 - 73
  • [49] Matrix-Tilted Archimedean Copulas
    Hofert, Marius
    Ziegel, Johanna F.
    [J]. RISKS, 2021, 9 (04)
  • [50] Archimedean copulas: Prescriptions and proscriptions.
    Nelsen, Roger B.
    [J]. INSURANCE MATHEMATICS & ECONOMICS, 2006, 39 (03): : 399 - 399