Testing class Logistic
checkConstructorAndDestructor()
checkCopyConstructor()
streamObject(const T & anObject)
class=Logistic name=Logistic dimension=1 alpha=-0.5 beta=1.5
streamObject(const T & anObject)
class=Logistic name=Logistic dimension=1 alpha=-0.5 beta=1.5
areSameObjects(const T & firstObject, const T & secondObject)
areDifferentObjects(const T & firstObject, const T & secondObject)
Distribution class=Logistic name=Logistic dimension=1 alpha=-0.5 beta=1.5
Distribution Logistic(alpha = -0.5, beta = 1.5)
Elliptical = true
Continuous = true
oneRealization=class=Point name=Unnamed dimension=1 values=[0.297531]
oneSample first=class=Point name=Unnamed dimension=1 values=[2.5289] last=class=Point name=Unnamed dimension=1 values=[-0.955127]
mean=class=Point name=Unnamed dimension=1 values=[-0.472451]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[7.49258]
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=[-0.0403812]
ddf (FD)=class=Point name=Unnamed dimension=1 values=[-0.0403812]
log pdf=-2.03199
pdf     =0.131075
pdf (FD)=0.131075
cdf=0.731059
ccdf=0.268941
survival=0.268941
Inverse survival=class=Point name=Unnamed dimension=1 values=[-4.91666]
Survival(inverse survival)=0.95
characteristic function=(0.074307,-0.0405941)
log characteristic function=(-2.46897,-0.5)
pdf gradient     =class=Point name=Unnamed dimension=2 values=[0.0403812,-0.0470019]
pdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[0.0403812,-0.0470019]
cdf gradient     =class=Point name=Unnamed dimension=2 values=[-0.131075,-0.131075]
cdf gradient (FD)=class=Point name=Unnamed dimension=2 values=[-0.131075,-0.131075]
quantile=class=Point name=Unnamed dimension=1 values=[3.91666]
cdf(quantile)=0.95
Minimum volume interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-5.99534] upper bound=class=Point name=Unnamed dimension=1 values=[4.99534] 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(Logistic(alpha = -0.5, beta = 1.5)) gradientImplementation=MinimumVolumeLevelSetGradient(Logistic(alpha = -0.5, beta = 1.5)) hessianImplementation=class=CenteredFiniteDifferenceHessian name=Unnamed epsilon=class=Point name=Unnamed dimension=1 values=[0.0001] evaluation=MinimumVolumeLevelSetEvaluation(Logistic(alpha = -0.5, beta = 1.5)) level=4.11966
beta=0.01625
Bilateral confidence interval=class=Interval name=Unnamed dimension=1 lower bound=class=Point name=Unnamed dimension=1 values=[-5.99534] upper bound=class=Point name=Unnamed dimension=1 values=[4.99534] 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=[-48.8543] upper bound=class=Point name=Unnamed dimension=1 values=[3.91666] 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=[-4.91666] upper bound=class=Point name=Unnamed dimension=1 values=[47.8543] finite lower bound=[1] finite upper bound=[1]
beta=0.95
mean=class=Point name=Unnamed dimension=1 values=[-0.5]
standard deviation=class=Point name=Unnamed dimension=1 values=[2.7207]
skewness=class=Point name=Unnamed dimension=1 values=[0]
kurtosis=class=Point name=Unnamed dimension=1 values=[4.2]
covariance=class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[7.4022]
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=[[alpha : -0.5, beta : 1.5]]
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]
standard moment n=2, value=class=Point name=Unnamed dimension=1 values=[3.28987]
standard moment n=3, value=class=Point name=Unnamed dimension=1 values=[0]
standard moment n=4, value=class=Point name=Unnamed dimension=1 values=[45.4576]
standard moment n=5, value=class=Point name=Unnamed dimension=1 values=[0]
Standard representative=Logistic(alpha = 0, beta = 1)
