RVineHessian(VineCopula)
RVineHessian()所属R语言包：VineCopula
Hessian matrix of the log-likelihood of an R-vine copula model
Hessian矩阵R-藤Copula模型的对数似然
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function calculates the Hessian matrix of the log-likelihood of a d-dimensional R-vine copula model with respect to the copula parameter and evaluates it on a given copula data set.
此函数计算Hessian矩阵的对数似然的一个d维的R-葡萄树的copula模型与Copula函数的参数，并评估它在给定的Copula的数据集。
用法----------Usage----------
RVineHessian(data, RVM)
参数----------Arguments----------
参数：data
An N x d data matrix (with uniform margins).
一个N×d数据矩阵（均匀的利润）。
参数：RVM
An RVineMatrix object including the structure, the pair-copula families, and the parameters.
RVineMatrix对象包括结构，对Copula的家庭，和参数。
值----------Value----------
参数：hessian
The calculated Hessian matrix of the log-likelihood value of the R-vine copula model.
计算的Hessian矩阵的对数似然值的R-藤copula模型。
参数：der
The product of the gradient vector with its transposed version.
而其转置的版本的梯度向量的产物。
注意----------Note----------
The Hessian matrix is not available for R-vine copula models with two parameter Archimedean copulas, i.e. BB1, BB6, BB7, BB8 and their rotated versions.
Hessian矩阵是不可用藤R-Copula函数模型的两个参数阿基米德Copula函数，即BB1，BB6，BB7，BB8和旋转的版本。
（作者）----------Author(s)----------
Ulf Schepsmeier, Jakob Stoeber
参考文献----------References----------
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.
Selecting and estimating regular vine copulae and application to financial returns. Submitted for publication. http://mediatum.ub.tum.de/node?id=1079277
参见----------See Also----------
BiCopDeriv, BiCopDeriv2, BiCopHfuncDeriv, BiCopHfuncDeriv2, <br>
BiCopDeriv，BiCopDeriv2，BiCopHfuncDeriv，BiCopHfuncDeriv2，参考
实例----------Examples----------
# define 5-dimensional R-vine tree structure matrix[定义5维的R-葡萄树结构矩阵]
Matrix = c(5,2,3,1,4,0,2,3,4,1,0,0,3,4,1,0,0,0,4,1,0,0,0,0,1)
Matrix = matrix(Matrix,5,5)
# define R-vine pair-copula family matrix[定义R-藤对Copula的家庭矩阵]
family = c(0,1,3,4,4,0,0,3,4,1,0,0,0,4,1,0,0,0,0,3,0,0,0,0,0)
family = matrix(family,5,5)
# define R-vine pair-copula parameter matrix[定义R-藤对Copula函数的参数矩阵]
par = c(0,0.2,0.9,1.5,3.9,0,0,1.1,1.6,0.9,0,0,0,1.9,0.5,
0,0,0,0,4.8,0,0,0,0,0)
par = matrix(par,5,5)
# define second R-vine pair-copula parameter matrix[定义第二个R-藤对Copula函数的参数矩阵]
par2 = matrix(0,5,5)
# define RVineMatrix object[定义RVineMatrix对象]
RVM = RVineMatrix(Matrix=Matrix,family=family,par=par,par2=par2,
names=c("V1","V2","V3","V4","V5"))
# simulate a sample of size 300 from the R-vine copula model[从R-藤copula模型，模拟样品的尺寸为300]
simdata = RVineSim(300,RVM)
# compute the Hessian matrix of the first row of the data[计算Hessian矩阵的第一行中的数据]
out2 = RVineHessian(simdata[1,],RVM)
out2$hessian
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 16:11:37
