RVineVuongTest(VineCopula)
RVineVuongTest()所属R语言包：VineCopula
Vuong test comparing two R-vine copula models
王测试比较两个R-藤Copula函数模型
译者：生物统计家园网 机器人LoveR
描述----------Description----------
This function performs a Vuong test between two d-dimensional R-vine copula models as specified by their RVineMatrix objects.
这个函数执行Vuong的测试两d维的R-RVineMatrix对象所指定的藤Copula函数模型。
用法----------Usage----------
RVineVuongTest(data, RVM1, RVM2)
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：RVM1, RVM2
RVineMatrix objects of models 1 and 2.
RVineMatrix对象模型1和2。
Details
详细信息----------Details----------
The likelihood-ratio based test proposed by Vuong (1989) can be used for comparing non-nested models. For this let c_1 and c_2 be two competing vine copulas in terms of their densities and with estimated parameter sets θ_1 and θ_2. We then compute the standardized sum, ν, of the log differences of their pointwise likelihoods m_i:=log[c_1(u_i|θ_1) / c_2(u_i|θ_2) ] for observations u_i in [0,1],i=1,...,N , i.e.,
者Vuong的（1989）提出的基于似然比测试，可以使用非嵌套模型进行比较。这让c_1和c_2是两个相互竞争的藤Copula函数，其密度和估计的参数设置θ_1和θ_2。然后，我们计算规范的总和，ν“的log差异的逐点似然m_i:=log[c_1(u_i|θ_1) / c_2(u_i|θ_2) ]的观测u_i in [0,1],i=1,...,N，即，
Vuong (1989) shows that ν is asymptotically standard normal. According to the null-hypothesis
王街（1989）ν渐近标准正常。根据零假设
we hence prefer vine model 1 to vine model 2 at level α if
因此，我们更喜欢藤模型1模型2藤级α，如果
where Φ^{-1} denotes the inverse of the standard normal distribution function. If ν<-Φ^{-1}(1-α/2) we choose model 2. If, however, |ν| <= Φ^{-1}(1-α/2), no decision among the models is possible.
Φ^{-1}表示标准正态分布函数的逆。如果ν<-Φ^{-1}(1-α/2)的“我们选择模式2。然而，如果|ν| <= Φ^{-1}(1-α/2)，模型之间没有决定是可能的。
Like AIC and BIC, the Vuong test statistic may be corrected for the number of parameters used in the models. There are two possible corrections; the Akaike and the Schwarz corrections, which correspond to the penalty terms in the AIC and the BIC, respectively.
喜欢AIC和BIC，王的检验统计量可以校正为在模型中使用的参数的数目。有两种可能的改正;的Akaike和施瓦茨更正，这对应于在AIC和BIC的惩罚项，分别。
值----------Value----------
参数：statistic, statistic.Akaike, statistic.Schwarz
Test statistics without correction, with Akaike correction and with Schwarz correction.
测试统计数据未经修正，与赤池的校正和与施瓦茨校正。
参数：p.value, p.value.Akaike, p.value.Schwarz
P-values of tests without correction, with Akaike correction and with Schwarz correction.
P值的测试，而无需校正，与赤池校正与施瓦茨校正。
（作者）----------Author(s)----------
Jeffrey Dissmann, Eike Brechmann
参考文献----------References----------
Ratio tests for model selection and non-nested hypotheses. Econometrica 57 (2), 307-333.
参见----------See Also----------
RVineClarkeTest, RVineAIC, RVineBIC
RVineClarkeTest，RVineAIC，RVineBIC
实例----------Examples----------
# define first 5-dimensional RVineMatrix object[第一个定义的5维RVineMatrix，对象]
Matrix1 = 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)
Matrix1 = matrix(Matrix1,5,5)
family1 = 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)
family1 = matrix(family1,5,5)
par1 = 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)
par1 = matrix(par1,5,5)
RVM1 = RVineMatrix(Matrix=Matrix1,family=family1,par=par1,
par2=matrix(0,5,5),names=c("V1","V2","V3","V4","V5"))
# define second 5-dimensional RVineMatrix object[第二个定义的5维RVineMatrix，对象]
Matrix2 = c(5,4,3,2,1,0,4,3,2,1,0,0,3,2,1,0,0,0,2,1,0,0,0,0,1)
Matrix2 = matrix(Matrix2,5,5)
family2 = c(0,3,1,3,2,0,0,1,5,3,0,0,0,2,3,0,0,0,0,1,0,0,0,0,0)
family2 = matrix(family2,5,5)
par2 = c(0,0.8,0.3,1.5,0.8,0,0,-0.4,1.6,1.2,0,0,0,-0.4,1.5,
0,0,0,0,0.6,0,0,0,0,0)
par2 = matrix(par2,5,5)
nu2 = c(0,0,0,0,5,0,0,0,0,0,0,0,0,8,0,0,0,0,0,0,0,0,0,0,0)
nu2 = matrix(nu2,5,5)
RVM2 = RVineMatrix(Matrix=Matrix2,family=family2,par=par2,par2=nu2,
names=c("V1","V2","V3","V4","V5"))
# simulate a sample of size 300 from the first R-vine copula model[从第一个R-藤Copula模型模拟样品的尺寸为300]
simdata = RVineSim(300,RVM1)
# compare the two models based on this sample[比较这两种模型基于此示例]
vuong = RVineVuongTest(simdata,RVM1,RVM2)
vuong$statistic
vuong$statistic.Schwarz
vuong$p.value
vuong$p.value.Schwarz
转载请注明:出自 生物统计家园网(http://www.biostatistic.net)。
注：
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发表于 2012-10-1 16:12:37
