Measures of Spread
Variance
(標本分散)σ2=1nn∑i=1(xi−m)2
(不偏分散)σ2=1n−1n∑i=1(xi−m)2
Standard deviation
(Standard Deviation of the Sample)σ=√1nn∑i=1(xi−m)2
(Sample Standard Deviation)σ=√1n−1n∑i=1(xi−m)2
Interquartile Range
IQR=Q3−Q1
Measures of Shape
Coefficient of Skewness
CS=1n∑ni=1(xi−m)3σ3
Coefficient of Variation
CV=σm
Comparing Two Distributions
q-q plot
Scatterplot
Correlation
Correlation Coefficient
ρ=1n∑ni=1(xi−mx)(yi−my)σxσy
Covariance
C=1nn∑i=1(xi−mx)(yi−my)
Moment of Inertia
γ=12nn∑i=1(xi−yi)2
Linear Regression
y=ax+b
a=ρσyσx
b=my−amx
Spatial Description
h-Scatterplot V(t)とV(t+h)の分布
x=V(t)= vi、y=V(t+h)= vjとすると、Moment of Inertia = Semivariogramとなる。(Semiは1/2の意)
Semivariogram
γ(h)=12N(h)∑(i,j)|hij=h(vi−vj)2
Kriging
Simple kriging
ZSK(u)−m=n(u)∑α=1λSKα(u)[Z(uα)−m]
Ordinary kriging
ZOK(u)=n(u)∑α=1λOKα(u)Z(uα)
Indicator Kriging
The ordinary kriging of indicators at several cutoffs, using a separatevariogram model for each cutoff, is usually referred to simply asindicator kriging.
The ordinary kriging of indicators at several cutoffs, using a separatevariogram model for each cutoff, is usually referred to simply asindicator kriging.
Median Indicator Kriging
There is an approximation to indicator kriging that, in many situations,produces very good results. This approximation consists of using thesame variogram model for the estimation at all cutoffs. The variogrammodel chosen for all cutoffs is most commonly developed from theindicator data at a cutoff close to the median. This procedure isusually referred to as median indicator kriging.
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20190727
TEX 箇所を表示できるように修正しました。
There is an approximation to indicator kriging that, in many situations,produces very good results. This approximation consists of using thesame variogram model for the estimation at all cutoffs. The variogrammodel chosen for all cutoffs is most commonly developed from theindicator data at a cutoff close to the median. This procedure isusually referred to as median indicator kriging.
************************************
20190727
TEX 箇所を表示できるように修正しました。
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