1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
|
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]
Point= class=Point name=Unnamed dimension=1 values=[1]
ddf = class=Point name=Unnamed dimension=1 values=[-0.12381]
log pdf=-2.168831
pdf =0.114311
cdf=0.825615
ccdf=0.174385
characteristic function= (0.74087680493-0.0157786395662j)
pdf gradient = 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]
quantile= class=Point name=Unnamed dimension=1 values=[3.83742]
cdf(quantile)=0.950000
InverseSurvival= class=Point name=Unnamed dimension=1 values=[-0.468798]
Survival(inverseSurvival)=0.950000
entropy=0.824404
Minimum volume interval= [-0.499653, 3.83753]
threshold= [0.95]
Minimum volume level set= {x | f(x) <= 4.14451} with f=
MinimumVolumeLevelSetEvaluation(LogNormal(muLog = -1, sigmaLog = 1.5, gamma = -0.5))
beta= [0.0158512]
Bilateral confidence interval= [-0.480551, 6.45838]
beta= [0.95]
Unilateral confidence interval (lower tail)= [-0.5, 3.83742]
beta= [0.95]
Unilateral confidence interval (upper tail)= [-0.468798, 35451.8]
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]
parameters= [class=PointWithDescription name=X0 dimension=3 description=[muLog,sigmaLog,gamma] values=[-1,1.5,-0.5]]
Standard representative= LogNormal(muLog = -1, sigmaLog = 1.5, gamma = 0)
|
