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Distribution class=GeneralizedPareto name=GeneralizedPareto dimension=1 sigma=1.5 xi=-0.2 u=0.5
Distribution GeneralizedPareto(sigma = 1.5, xi=-0.2, u=0.5)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[1.16226]
oneSample first= class=Point name=Unnamed dimension=1 values=[0.684665] last= class=Point name=Unnamed dimension=1 values=[1.68051]
mean= class=Point name=Unnamed dimension=1 values=[1.74428]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[1.12783]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
pdf = 0.376111934156
cdf= 0.511054485597
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.096439,-0.188281,0.231453]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.250741,-0.131347,-0.376112]
quantile= class=Point name=Unnamed dimension=1 values=[3.8804]
cdf(quantile)= 0.95
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.576547]
Survival(inverseSurvival)=0.950000
entropy=1.205465
Minimum volume interval= [0.5, 3.8804]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 2.80205} with f=
MinimumVolumeLevelSetEvaluation(GeneralizedPareto(sigma = 1.5, xi=-0.2, u=0.5))
beta= [0.0606855]
Bilateral confidence interval= [0.537881, 4.41368]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0.5, 3.8804]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.576547, 8]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[1.75]
standard deviation= class=Point name=Unnamed dimension=1 values=[1.05644]
skewness= class=Point name=Unnamed dimension=1 values=[1.18322]
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=[1.11607]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[sigma,xi,u] values=[1.5,-0.2,0.5]]
Standard representative= GeneralizedPareto(sigma = 1, xi=-0.2, u=0)
Distribution class=GeneralizedPareto name=GeneralizedPareto dimension=1 sigma=1.5 xi=0 u=0.5
Distribution GeneralizedPareto(sigma = 1.5, xi=0, u=0.5)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.633808]
oneSample first= class=Point name=Unnamed dimension=1 values=[5.22134] last= class=Point name=Unnamed dimension=1 values=[1.0016]
mean= class=Point name=Unnamed dimension=1 values=[1.97709]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[2.14949]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
pdf = 0.342278079355
cdf= 0.486582880967
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.0760618,-0.152124,0.228185]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.228185,-0.114093,-0.342278]
quantile= class=Point name=Unnamed dimension=1 values=[4.9936]
cdf(quantile)= 0.95
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.57694]
Survival(inverseSurvival)=0.950000
entropy=1.405465
Minimum volume interval= [0.5, 4.9936]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 3.4012} with f=
MinimumVolumeLevelSetEvaluation(GeneralizedPareto(sigma = 1.5, xi=0, u=0.5))
beta= [0.0333333]
Bilateral confidence interval= [0.537977, 6.03332]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0.5, 4.9936]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.57694, 54.5655]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[2]
standard deviation= class=Point name=Unnamed dimension=1 values=[1.5]
skewness= class=Point name=Unnamed dimension=1 values=[2]
kurtosis= class=Point name=Unnamed dimension=1 values=[9]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[2.25]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[sigma,xi,u] values=[1.5,0,0.5]]
Standard representative= GeneralizedPareto(sigma = 1, xi=0, u=0)
Distribution class=GeneralizedPareto name=GeneralizedPareto dimension=1 sigma=1.5 xi=0.2 u=0.5
Distribution GeneralizedPareto(sigma = 1.5, xi=0.2, u=0.5)
asPareto= Pareto(beta = 7.5, alpha=5, gamma=-7)
Elliptical = False
Continuous = True
oneRealization= class=Point name=Unnamed dimension=1 values=[0.635009]
oneSample first= class=Point name=Unnamed dimension=1 values=[7.07521] last= class=Point name=Unnamed dimension=1 values=[1.01876]
mean= class=Point name=Unnamed dimension=1 values=[2.33451]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[5.26239]
Point= class=Point name=Unnamed dimension=1 values=[1.5]
pdf = 0.31460293288
cdf= 0.465175014104
pdf gradient = class=Point name=Unnamed dimension=3 values=[-0.0616868,-0.125946,0.222073]
cdf gradient = class=Point name=Unnamed dimension=3 values=[-0.209735,-0.100495,-0.314603]
quantile= class=Point name=Unnamed dimension=1 values=[6.65423]
cdf(quantile)= 0.95
InverseSurvival= class=Point name=Unnamed dimension=1 values=[0.577336]
Survival(inverseSurvival)=0.950000
entropy=1.605465
Minimum volume interval= [0.5, 6.65423]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 4.00034} with f=
MinimumVolumeLevelSetEvaluation(GeneralizedPareto(sigma = 1.5, xi=0.2, u=0.5))
beta= [0.0183093]
Bilateral confidence interval= [0.538073, 8.68459]
beta= [0.95]
Unilateral confidence interval (lower tail)= [0.5, 6.65423]
beta= [0.95]
Unilateral confidence interval (upper tail)= [0.577336, 10126.8]
beta= [0.95]
mean= class=Point name=Unnamed dimension=1 values=[2.375]
standard deviation= class=Point name=Unnamed dimension=1 values=[2.42061]
skewness= class=Point name=Unnamed dimension=1 values=[4.64758]
kurtosis= class=Point name=Unnamed dimension=1 values=[73.8]
covariance= class=CovarianceMatrix dimension=1 implementation=class=MatrixImplementation name=Unnamed rows=1 columns=1 values=[5.85938]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[sigma,xi,u] values=[1.5,0.2,0.5]]
Standard representative= GeneralizedPareto(sigma = 1, xi=0.2, u=0)
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