RVineClarkeTest(VineCopula)
RVineClarkeTest()所属R语言包：VineCopula
Clarke test comparing two R-vine copula models
克拉克测试比较两个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 Clarke test between two d-dimensional R-vine copula models as specified by their RVineMatrix objects.
这个函数执行克拉克测试两d维的R-RVineMatrix对象所指定的藤Copula函数模型。
用法----------Usage----------
RVineClarkeTest(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 test proposed by Clarke (2007) allows to compare 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. The null hypothesis of statistical indistinguishability of the two models is
由克拉克（2007）提出的测试，可以比较非嵌套模型。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。统计不可区分这两个模型的零假设是
where m_i:=log[ c_1(u_i|θ_1) / c_2(u_i|θ_2) ] for observations u_i, i=1,...,N.
其中m_i:=log[ c_1(u_i|θ_1) / c_2(u_i|θ_2) ]的观测u_i, i=1,...,N。
Since under statistical equivalence of the two models the log likelihood ratios of the single observations are uniformly distributed around zero and in expectation 50\% of the log likelihood ratios greater than zero, the tets statistic
由于根据两个模型统计等价性的对数似然比的单个观测被均匀地分布在零附近，并在期望50\%对数似然比大于零，四面体统计
where 1 is the indicator function, is distributed Binomial with parameters N and p=0.5, and critical values can easily be obtained. Model 1 is interpreted as statistically equivalent to model 2 if B is not significantly different from the expected value np=N/2.
1的指标是函数，分布二项式与参数N和p=0.5，和临界值可以很容易地获得。模型1被解释为等效模型2如果B是显着不同的预期值np=N/2。
Like AIC and BIC, the Clarke 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，克拉克的检验统计量可能被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----------
A Simple Distribution-Free Test for Nonnested Model Selection. Political Analysis, 15, 347-363.
参见----------See Also----------
RVineVuongTest, RVineAIC, RVineBIC
RVineVuongTest，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]
clarke = RVineClarkeTest(simdata,RVM1,RVM2)
clarke$statistic
clarke$statistic.Schwarz
clarke$p.value
clarke$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 16:11:19
