Testing class LogNormal
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=LogNormal name=LogNormal dimension=1 muLog=-1 sigmaLog=1.5 gamma=-0.5
streamObject(const T & anObject)
class=LogNormal name=LogNormal dimension=1 muLog=-1 sigmaLog=1.5 gamma=-0.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=LogNormal name=LogNormal dimension=1 muLog=-1 sigmaLog=1.5 gamma=-0.5
Distribution LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5)
Elliptical = false
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.416038]
oneSample first=class=Point name=Unnamed dimension=1 values=[-0.444936] last=class=Point name=Unnamed dimension=1 values=[3.12047]
mean=class=Point name=Unnamed dimension=1 values=[0.609446]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[8.23002]
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=[-0.12381]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.12381]
log pdf=-2.16883
pdf     =0.114311
pdf (FD)=0.114311
cdf=0.825615
ccdf=0.174385
survival=0.174385
Inverse survival=class=Point name=Unnamed dimension=1 values=[-0.468798]
Survival(inverse survival)=0.95
characteristic function=(0.740877,-0.0157786)
log characteristic function=(-0.299694,-0.021294)
pdf gradient     =class=Point name=Unnamed dimension=3 values=[0.0714046,-0.00930299,0.12381]
pdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[0.0714046,-0.00930299,0.12381]
cdf gradient     =class=Point name=Unnamed dimension=3 values=[-0.171467,-0.16066,-0.114311]
cdf gradient (FD)=class=Point name=Unnamed dimension=3 values=[-0.171467,-0.16066,-0.114311]
quantile=class=Point name=Unnamed dimension=1 values=[3.83742]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.499653] upper bound=class=Point name=Unnamed dimension=1 values=[3.83753] 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(LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5)) gradientImplementation=MinimumVolumeLevelSetGradient(LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5)) level=4.14451
beta=0.0158512
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-0.480551] upper bound=class=Point name=Unnamed dimension=1 values=[6.45838] 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=[-0.5] upper bound=class=Point name=Unnamed dimension=1 values=[3.83742] 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=[-0.468798] upper bound=class=Point name=Unnamed dimension=1 values=[35451.8] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[0.633148]
standard deviation=class=Point name=Unnamed dimension=1 values=[3.30128]
skewness=class=Point name=Unnamed dimension=1 values=[33.468]
kurtosis=class=Point name=Unnamed dimension=1 values=[10078.3]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[10.8985]
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=[[muLog : -1, sigmaLog : 1.5, gamma : -0.5]]
parameters=[[muLog : -1, sigmaLog : 1.5, gamma : -0.5]]
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=[1.13315]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[12.1825]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[1242.65]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[1.2026e+06]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[1.10423e+10]
Standard representative=LogNormal(muLog = -1, sigmaLog = 1.5, gamma = 0)
