Testing class NonCentralChiSquare
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=NonCentralChiSquare name=NonCentralChiSquare dimension=1 nu=1.5 lambda=2.5
streamObject(const T & anObject)
class=NonCentralChiSquare name=NonCentralChiSquare dimension=1 nu=1.5 lambda=2.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=NonCentralChiSquare name=NonCentralChiSquare dimension=1 nu=1.5 lambda=2.5
Distribution NonCentralChiSquare(nu = 1.5, lambda = 2.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[5.14874]
oneSample first=class=Point name=Unnamed dimension=1 values=[10.3181] last=class=Point name=Unnamed dimension=1 values=[3.44075]
mean=class=Point name=Unnamed dimension=1 values=[4.01363]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[12.6416]
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=[-0.0277166]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.0277166]
log pdf=-1.78305
pdf     =0.168125
pdf (FD)=0.168125
cdf=0.205328
ccdf=0.794672
survival=0.794672
Inverse survival=class=Point name=Unnamed dimension=1 values=[0.169073]
Survival(inverse survival)=0.95
characteristic function=(0.0479067,0.195396)
log characteristic function=(-1.60354,1.33036)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.0269116,-0.0447116]
pdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.0269116,-0.0447116]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.127286,-0.0787015]
cdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.127286,-0.0787015]
quantile=class=Point name=Unnamed dimension=1 values=[11.1286]
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=[11.1286] 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(NonCentralChiSquare(nu = 1.5, lambda = 2.5)) gradientImplementation=MinimumVolumeLevelSetGradient(NonCentralChiSquare(nu = 1.5, lambda = 2.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(NonCentralChiSquare(nu = 1.5, lambda = 2.5)) level=4.1499
beta=0.015766
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[0.068294] upper bound=class=Point name=Unnamed dimension=1 values=[13.2884] 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=[11.1286] 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=[0.169073] upper bound=class=Point name=Unnamed dimension=1 values=[127] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[4]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[13]
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, lambda : 2.5]]
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=[4]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[29]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[292]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[3715]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[56780]
Standard representative=NonCentralChiSquare(nu = 1.5, lambda = 2.5)
nu=1.5
standard deviation=class=Point name=Unnamed dimension=1 values=[3.60555]
skewness=class=Point name=Unnamed dimension=1 values=[1.53609]
kurtosis=class=Point name=Unnamed dimension=1 values=[6.26627]
