VineCopula-package(VineCopula)
VineCopula-package()所属R语言包：VineCopula
Statistical inference of vine copulas
藤Copula函数的统计推断
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This package provides functions for statistical inference of vine copulas. It contains tools for bivariate exploratory data analysis, bivariate copula selection and (vine) tree construction. Models can be estimated either sequentially or by joint maximum likelihood estimation. Sampling algorithms and plotting methods are also included. Data is assumed to lie in the unit hypercube (so-called copula data). For C- and D-vines links to the package CDVine are provided.
这个包提供的功能藤Copula函数的统计推断。它包含的工具二元探索性数据分析，二元Copula的选择和树（藤）建设。可以估算模型顺序或联合最大似然估计。采样算法和绘图方法也包括在内。数据被假定为位于单位超立方体（所谓的系词数据）。 C-D-葡萄树的包CDVine的链接。
Details
详细信息----------Details----------
备注----------Remark----------
The package VineCopula is a continuation of the package CDVine by U. Schepsmeier and E. C. Brechmann. It includes all functions implemented in CDVine for the bivariate case (BiCop-functions).
该的包VineCopula是延续了包CDVine，由U. Schepsmeier和EC Brechmann。它包括所有在CDVine的二元情况（BiCop功能）的实现的功能。
二元Copula的家庭的----------Bivariate copula families----------
In this package several bivariate copula families are included for bivariate analysis as well as for multivariate analysis using vine copulas. It provides functionality of elliptical (Gaussian and Student-t) as well as Archimedean (Clayton, Gumbel, Frank, Joe, BB1, BB6, BB7 and BB8) copulas to cover a large bandwidth of possible dependence structures. For the Archimedean copula families rotated versions are included to cover negative dependence too. The two parameter BB1, BB6, BB7 and BB8 copulas are however numerically instable for large parameters, in particular, if BB6, BB7 and BB8 copulas are close to the Joe copula which is a boundary case of these three copula families. In general, the user should be careful with extreme parameter choices.
在这个包中，几个二元Copula的家庭都纳入二元分析，以及使用藤Copula函数进行多因素分析。它提供的功能的椭圆形（高斯和Student-T），以及阿基米德（克莱顿，Gumbel分布，弗兰克，乔，BB1，BB6，BB7，BB8）的Copula函数覆盖一个大的带宽可能依赖结构。对于阿基米德Copula家庭旋转的版本，以支付负相依。两个参数BB1，BB6，BB7，BB8 Copula函数但是大参数数值不稳定，尤其是，BB6，BB7，BB8 Copula函数接近乔Copula函数是有边界的情况下，这三种Copula的家庭的。在一般情况下，用户应该小心极端的参数选择。
The following table shows the parameter ranges of bivariate copula families with parameters par and par2:
下表显示的参数范围二元Copula的家庭，参数par和par2：
par
par
(-1,1)
(-1,1)
(-1,1)
(-1,1)
(0,∞)
(0,∞)
[1,∞)
[1,∞)
R\backslash\{0\}
R\backslash\{0\}
(1,∞)
(1,∞)
(-∞,0)
(-∞,0)
(-∞,-1]
(-∞,-1]
(-∞,-1)
(-∞,-1)
(0,∞)
(0,∞)
[1,∞)
[1,∞)
[1,∞)
[1,∞)
[1,∞)
[1,∞)
(-∞,0)
(-∞,0)
(-∞,-1]
(-∞,-1]
(-∞,-1]
(-∞,-1]
(-∞,-1]
(-∞,-1]
R-藤Copula函数模型----------R-vine copula models----------
The specification of an R-vine is done in matrix notation, introduced by Dissmann et al. (2011). One matrix contains the R-vine tree structure, one the copula families utilized and two matrices corresponding parameter values. These four matrices are stored in an RVineMatrix object created by the function RVineMatrix. Each matrix is a d x d lower triangular matrix. Since C- and D-vines are special cases, boundary cases, of R-vines one can write each C- or D-vine in R-vine notation. The transformation of notation to or from an R-vine can be done via C2RVine, D2RVine, R2CVine and R2DVine, which provide an interface to the package CDVine. For more details see the documentation of the functions.
的R-藤本说明书是在矩阵记法，引入由Dissmann等。 （2011年）。一个矩阵包含了R-的葡萄树结构，利用Copula函数的家庭和两个矩阵对应的参数值。这四个矩阵存储在RVineMatrix对象创建由函数RVineMatrix中。每个矩阵的d x深下三角矩阵。由于C-D-葡萄树是特殊的情况下，边界的情况下，R-葡萄树，可以写每个C-D-葡萄树在R-葡萄树符号。被可以通过C2RVine, D2RVine, R2CVine和R2DVine，它提供了一个接口的包CDVine，转型的符号或R-藤。有关详细信息，请参阅文档的功能。
承认----------Acknowledgment----------
We acknowledge substantial contributions by our working group at Technische Universitaet Muenchen, in particular by Carlos Almeida and Aleksey Min. In addition, we like to thank Shing (Eric) Fu, Feng Zhu, Guang (Jack) Yang, and Harry Joe for providing their implementation of the method by Knight (1966) for efficiently computing the empirical Kendall's tau. We are especially grateful to Harry Joe for his contributions to the implementation of the bivariate Archimedean copulas.
我们承认我们的工作小组在慕尼黑工业大学的重大贡献，特别是卡洛斯·阿尔梅达和Aleksey最小。此外，我们要感谢城（ERIC）富，朱峰，广（杰克）杨和哈里·乔骑士（1966年）为有效地计算经验Kendall的tau提供其实施的方法。我们要特别感谢哈利·乔，他的贡献的二元阿基米德Copula函数的实施。
（作者）----------Author(s)----------
Ulf Schepsmeier, Jakob Stoeber, Eike Christian Brechmann <VineCopula@ma.tum.de>
参考文献----------References----------
Pair-copula constructions of multiple dependence. Insurance: Mathematics and Economics 44 (2), 182-198.
Probability density decomposition for conditionally dependent random variables modeled by vines. Annals of Mathematics and Artificial intelligence 32, 245-268.
Vines - a new graphical model for dependent random variables. Annals of Statistics 30, 1031-1068.
Truncated regular vines in high dimensions with applications to financial data. Canadian Journal of Statistics 40 (1), 68-85.
Risk management with high-dimensional vine copulas: An Analysis of the Euro Stoxx 50. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079276.
Maximum likelihood estimation of mixed C-vines with application to exchange rates. Statistical Modelling, 12(3), 229-255.
Selecting and estimating regular vine copulae and application to financial returns. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079277.
Families of m-variate distributions with given margins and m(m-1)/2 bivariate dependence parameters. In L. Rueschendorf, B. Schweizer, and M. D. Taylor (Eds.), Distributions with fixed marginals and related topics, pp. 120-141. Hayward: Institute of Mathematical Statistics.
Multivariate Models and Dependence Concepts. Chapman and Hall, London.
A computer method for calculating Kendall's tau with ungrouped data. Journal of the American Statistical Association 61 (314), 436-439.
Uncertainty Analysis with High Dimensional Dependence Modelling. Chichester: John Wiley.
DEPENDENCE MODELING: Vine Copula Handbook. Singapore: World Scientific Publishing Co.
Derivatives and Fisher information of bivariate copulas. Submitted for publication. http://mediatum.ub.tum.de/node?id=1106541.
Is there significant time-variation in multivariate dependence? In preparation. http://de.arxiv.org/abs/1205.4841.
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
注1：为了方便大家学习，本文档为生物统计家园网机器人LoveR翻译而成，仅供个人R语言学习参考使用，生物统计家园保留版权。
注2：由于是机器人自动翻译，难免有不准确之处，使用时仔细对照中、英文内容进行反复理解，可以帮助R语言的学习。
注3：如遇到不准确之处，请在本贴的后面进行回帖，我们会逐渐进行修订。
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发表于 2012-10-1 16:12:48
