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Testing class ChiSquare
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=ChiSquare name=ChiSquare dimension=1 nu=1.5
streamObject(const T & anObject)
class=ChiSquare name=ChiSquare dimension=1 nu=1.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=ChiSquare name=ChiSquare dimension=1 nu=1.5
Distribution ChiSquare(nu = 1.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[2.77998]
oneSample first=class=Point name=Unnamed dimension=1 values=[2.51653] last=class=Point name=Unnamed dimension=1 values=[1.04578]
mean=class=Point name=Unnamed dimension=1 values=[1.49285]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[2.84211]
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=[1]
ddf =class=Point name=Unnamed dimension=1 values=[-0.220728]
log pdf=-1.22314
pdf =0.294304
pdf (FD)=0.294304
cdf=0.527937
ccdf=0.472063
survival=0.472063
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.0332328]
Survival(inverse survival)=0.95
characteristic function=(0.368925,0.403688)
log characteristic function=(-0.603539,0.830362)
pdf gradient =class=Point name=Unnamed dimension=1 values=[0.0577886]
pdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[0.0577886]
cdf gradient =class=Point name=Unnamed dimension=1 values=[-0.291714]
cdf gradient (FD)=class=Point name=Unnamed dimension=1 values=[-0.291714]
quantile=class=Point name=Unnamed dimension=1 values=[4.9802]
cdf(quantile)=0.95
Minimum volume interval=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=[4.9802] 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,-logPDF] evaluationImplementation=MinimumVolumeLevelSetEvaluation(ChiSquare(nu = 1.5)) gradientImplementation=MinimumVolumeLevelSetGradient(ChiSquare(nu = 1.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(ChiSquare(nu = 1.5)) level=3.61461
beta=0.0269275
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.013113] upper bound=class=Point name=Unnamed dimension=1 values=[6.27581] 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=[4.9802] 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=[0.0332328] upper bound=class=Point name=Unnamed dimension=1 values=[39.9307] finite lower bound=[1] finite upper bound=[1]
beta=0.95
entropy=1.37496
entropy (MC)=1.37461
mean=class=Point name=Unnamed dimension=1 values=[1.5]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[3]
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 : 1.5]]
Standard representative=Gamma(k = 0.75, lambda = 1, gamma = 0)
nu=1.5
standard deviation=class=Point name=Unnamed dimension=1 values=[1.73205]
skewness=class=Point name=Unnamed dimension=1 values=[2.3094]
kurtosis=class=Point name=Unnamed dimension=1 values=[11]
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