Testing class InverseChiSquare
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=InverseChiSquare name=InverseChiSquare dimension=1 nu=1.5
streamObject(const T & anObject)
class=InverseChiSquare name=InverseChiSquare dimension=1 nu=1.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=InverseChiSquare name=InverseChiSquare dimension=1 nu=10.5
Distribution InverseChiSquare(nu = 10.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.0782188]
oneSample first=class=Point name=Unnamed dimension=1 values=[0.139041] last=class=Point name=Unnamed dimension=1 values=[0.137094]
mean=class=Point name=Unnamed dimension=1 values=[0.118211]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.00448375]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[0.190476]
ddf     =class=Point name=Unnamed dimension=1 values=[-32.6097]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-32.6097]
log pdf=0.538527
pdf     =1.71348
pdf (FD)=1.71348
cdf=0.898124
ccdf=0.101876
survival=0.101876
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.0526499]
Survival(inverse survival)=0.95
characteristic function=(0.999474,0.0224004)
log characteristic function=(-0.000274906,0.0224084)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[-1.01934,-6.74683]
pdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[-0.509672]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[0.0888995,0.163189]
cdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[0.0444498]
quantile=class=Point name=Unnamed dimension=1 values=[0.235]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0358836] upper bound=class=Point name=Unnamed dimension=1 values=[0.238469] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(InverseChiSquare(nu = 10.5)) gradientImplementation=MinimumVolumeLevelSetGradient(InverseChiSquare(nu = 10.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(InverseChiSquare(nu = 10.5)) level=35.0308
beta=6.11368e-16
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0471602] upper bound=class=Point name=Unnamed dimension=1 values=[0.283384] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[0.235] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0526499] upper bound=class=Point name=Unnamed dimension=1 values=[85.793] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.117647]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.00425872]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
parameters=[[nu : 10.5]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[0.235294]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[0.0723982]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[0.032177]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[0.0257416]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[0.102966]
Standard representative=InverseChiSquare(nu = 10.5)
standard deviation=class=Point name=Unnamed dimension=1 values=[0.0652588]
skewness=class=Point name=Unnamed dimension=1 values=[3.20493]
kurtosis=class=Point name=Unnamed dimension=1 values=[35.5333]
Distribution class=InverseChiSquare name=InverseChiSquare dimension=1 nu=15
Distribution InverseChiSquare(nu = 15)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.0913064]
oneSample first=class=Point name=Unnamed dimension=1 values=[0.0472117] last=class=Point name=Unnamed dimension=1 values=[0.0823255]
mean=class=Point name=Unnamed dimension=1 values=[0.0764712]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.00106545]
Kolmogorov test for the generator, sample size=100 is accepted
Kolmogorov test for the generator, sample size=1000 is accepted
Point= class=Point name=Unnamed dimension=1 values=[0.133333]
ddf     =class=Point name=Unnamed dimension=1 values=[-67.8131]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-67.8131]
log pdf=0.643708
pdf     =1.90353
pdf (FD)=1.90353
cdf=0.942263
ccdf=0.0577369
survival=0.0577369
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.0400067]
Survival(inverse survival)=0.95
characteristic function=(0.999938,0.0102561)
log characteristic function=(-9.55898e-06,0.0102564)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[-1.18971,-10.7073]
pdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[-0.594853]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[0.0479461,0.126902]
cdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[0.023973]
quantile=class=Point name=Unnamed dimension=1 values=[0.137723]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0299545] upper bound=class=Point name=Unnamed dimension=1 values=[0.140394] finite lower bound=[1] finite upper bound=[1]
threshold=0.95
Minimum volume level set=class=LevelSet name=Unnamed dimension=1 function=class=Function name=Unnamed implementation=class=FunctionImplementation name=Unnamed description=[x0,y0] evaluationImplementation=MinimumVolumeLevelSetEvaluation(InverseChiSquare(nu = 15)) gradientImplementation=MinimumVolumeLevelSetGradient(InverseChiSquare(nu = 15)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(InverseChiSquare(nu = 15)) level=-0.39369
beta=1.48244
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.036379] upper bound=class=Point name=Unnamed dimension=1 values=[0.15969] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (lower tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0] upper bound=class=Point name=Unnamed dimension=1 values=[0.137723] finite lower bound=[1] finite upper bound=[1]
beta=0.95
Unilateral confidence interval (upper tail)=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.0400067] upper bound=class=Point name=Unnamed dimension=1 values=[10.2372] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.0769231]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[0.00107585]
correlation=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
spearman=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
kendall=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1]
parameters=[[nu : 15]]
standard moment n=0, value=class=Point name=Unnamed dimension=1 values=[1]
standard moment n=1, value=class=Point name=Unnamed dimension=1 values=[0.153846]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[0.027972]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[0.00621601]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[0.001776]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[0.000710401]
Standard representative=InverseChiSquare(nu = 15)
standard deviation=class=Point name=Unnamed dimension=1 values=[0.0328001]
skewness=class=Point name=Unnamed dimension=1 values=[2.08463]
kurtosis=class=Point name=Unnamed dimension=1 values=[13.0952]
